This analysis delves into global terrorism trends, exploring how terrorist activities have evolved over time and identifying regions with significant deviations from global patterns. By examining attack success rates, prevalent tactics, and regional variations, we aim to uncover key insights into the nature of terrorist incidents worldwide. This exploration utilizes interactive plots and geographic visualizations to enhance understanding and engagement.
The dataset, sourced from the Global Terrorism Database (GTD), provides comprehensive data on over 180,000 terrorist attacks from 1970 to 2017. Managed by the National Consortium for the Study of Terrorism and Responses to Terrorism (START), this open-source repository offers detailed information on both domestic and international incidents, enabling a thorough examination of global terrorism trends.
Key Features:
First
second
third
# Import Libraries
import pandas as pd
import numpy as np
import os
import chardet
import missingno as msno
import time
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import cartopy.crs as ccrs
import cartopy.feature
import io
import base64
from IPython.display import HTML
# plt.style.use('ggplot')
import warnings
warnings.filterwarnings('ignore')
# extra_addition
pd.set_option('display.max_columns', 1000) # Show 1000 columns
pd.set_option('display.max_rows', 1000) # Show 1000 rows
path = os.path.join(os.getcwd(),"globalterrorismdb_0718dist.csv")
path
'F:\\from_C\\proj_jupyter\\globalterrorismdb_0718dist.csv'
# If you don't have chardet installed, uncomment the line below to install it:
# !pip install chardet
with open(path,"rb") as obj:
res= chardet.detect(obj.read(10000))
print(res)
{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
start_time = time.time()
df = pd.read_csv("globalterrorismdb_0718dist.csv",encoding='ISO-8859-1',low_memory=False)
pandas_duration = time.time() - start_time
pandas_duration
18.8396315574646
df.head()
| eventid | iyear | imonth | iday | approxdate | extended | resolution | country | country_txt | region | region_txt | provstate | city | latitude | longitude | specificity | vicinity | location | summary | crit1 | crit2 | crit3 | doubtterr | alternative | alternative_txt | multiple | success | suicide | attacktype1 | attacktype1_txt | attacktype2 | attacktype2_txt | attacktype3 | attacktype3_txt | targtype1 | targtype1_txt | targsubtype1 | targsubtype1_txt | corp1 | target1 | natlty1 | natlty1_txt | targtype2 | targtype2_txt | targsubtype2 | targsubtype2_txt | corp2 | target2 | natlty2 | natlty2_txt | targtype3 | targtype3_txt | targsubtype3 | targsubtype3_txt | corp3 | target3 | natlty3 | natlty3_txt | gname | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | motive | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claimmode_txt | claim2 | claimmode2 | claimmode2_txt | claim3 | claimmode3 | claimmode3_txt | compclaim | weaptype1 | weaptype1_txt | weapsubtype1 | weapsubtype1_txt | weaptype2 | weaptype2_txt | weapsubtype2 | weapsubtype2_txt | weaptype3 | weaptype3_txt | weapsubtype3 | weapsubtype3_txt | weaptype4 | weaptype4_txt | weapsubtype4 | weapsubtype4_txt | weapdetail | nkill | nkillus | nkillter | nwound | nwoundus | nwoundte | property | propextent | propextent_txt | propvalue | propcomment | ishostkid | nhostkid | nhostkidus | nhours | ndays | divert | kidhijcountry | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | ransomnote | hostkidoutcome | hostkidoutcome_txt | nreleased | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 197000000001 | 1970 | 7 | 2 | NaN | 0 | NaN | 58 | Dominican Republic | 2 | Central America & Caribbean | NaN | Santo Domingo | 18.456792 | -69.951164 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 1 | Assassination | NaN | NaN | NaN | NaN | 14 | Private Citizens & Property | 68.0 | Named Civilian | NaN | Julio Guzman | 58.0 | Dominican Republic | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | MANO-D | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | 0 | 0 | 0 | 0 | NaN |
| 1 | 197000000002 | 1970 | 0 | 0 | NaN | 0 | NaN | 130 | Mexico | 1 | North America | Federal | Mexico city | 19.371887 | -99.086624 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 6 | Hostage Taking (Kidnapping) | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 45.0 | Diplomatic Personnel (outside of embassy, cons... | Belgian Ambassador Daughter | Nadine Chaval, daughter | 21.0 | Belgium | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23rd of September Communist League | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | 7.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 0.0 | NaN | NaN | NaN | Mexico | 1.0 | 800000.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | 0 | 1 | 1 | 1 | NaN |
| 2 | 197001000001 | 1970 | 1 | 0 | NaN | 0 | NaN | 160 | Philippines | 5 | Southeast Asia | Tarlac | Unknown | 15.478598 | 120.599741 | 4.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 1 | Assassination | NaN | NaN | NaN | NaN | 10 | Journalists & Media | 54.0 | Radio Journalist/Staff/Facility | Voice of America | Employee | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 3 | 197001000002 | 1970 | 1 | 0 | NaN | 0 | NaN | 78 | Greece | 8 | Western Europe | Attica | Athens | 37.997490 | 23.762728 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 46.0 | Embassy/Consulate | NaN | U.S. Embassy | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 16.0 | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosive | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 4 | 197001000003 | 1970 | 1 | 0 | NaN | 0 | NaN | 101 | Japan | 4 | East Asia | Fukouka | Fukouka | 33.580412 | 130.396361 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | -9.0 | NaN | NaN | 0.0 | 1 | 0 | 7 | Facility/Infrastructure Attack | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 46.0 | Embassy/Consulate | NaN | U.S. Consulate | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8 | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
df.shape
(181691, 135)
df.columns.to_list()
['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended', 'resolution', 'country', 'country_txt', 'region', 'region_txt', 'provstate', 'city', 'latitude', 'longitude', 'specificity', 'vicinity', 'location', 'summary', 'crit1', 'crit2', 'crit3', 'doubtterr', 'alternative', 'alternative_txt', 'multiple', 'success', 'suicide', 'attacktype1', 'attacktype1_txt', 'attacktype2', 'attacktype2_txt', 'attacktype3', 'attacktype3_txt', 'targtype1', 'targtype1_txt', 'targsubtype1', 'targsubtype1_txt', 'corp1', 'target1', 'natlty1', 'natlty1_txt', 'targtype2', 'targtype2_txt', 'targsubtype2', 'targsubtype2_txt', 'corp2', 'target2', 'natlty2', 'natlty2_txt', 'targtype3', 'targtype3_txt', 'targsubtype3', 'targsubtype3_txt', 'corp3', 'target3', 'natlty3', 'natlty3_txt', 'gname', 'gsubname', 'gname2', 'gsubname2', 'gname3', 'gsubname3', 'motive', 'guncertain1', 'guncertain2', 'guncertain3', 'individual', 'nperps', 'nperpcap', 'claimed', 'claimmode', 'claimmode_txt', 'claim2', 'claimmode2', 'claimmode2_txt', 'claim3', 'claimmode3', 'claimmode3_txt', 'compclaim', 'weaptype1', 'weaptype1_txt', 'weapsubtype1', 'weapsubtype1_txt', 'weaptype2', 'weaptype2_txt', 'weapsubtype2', 'weapsubtype2_txt', 'weaptype3', 'weaptype3_txt', 'weapsubtype3', 'weapsubtype3_txt', 'weaptype4', 'weaptype4_txt', 'weapsubtype4', 'weapsubtype4_txt', 'weapdetail', 'nkill', 'nkillus', 'nkillter', 'nwound', 'nwoundus', 'nwoundte', 'property', 'propextent', 'propextent_txt', 'propvalue', 'propcomment', 'ishostkid', 'nhostkid', 'nhostkidus', 'nhours', 'ndays', 'divert', 'kidhijcountry', 'ransom', 'ransomamt', 'ransomamtus', 'ransompaid', 'ransompaidus', 'ransomnote', 'hostkidoutcome', 'hostkidoutcome_txt', 'nreleased', 'addnotes', 'scite1', 'scite2', 'scite3', 'dbsource', 'INT_LOG', 'INT_IDEO', 'INT_MISC', 'INT_ANY', 'related']
# rename some columns which needed it to exploring.
df.rename(columns={'eventid':'id','iyear':'Year','imonth':'Month','iday':'Day','country_txt':'Country','provstate':'State',
'region_txt':'Region','attacktype1_txt':'AttackType','target1':'Target','nkill':'Killed',
'nwound':'Wounded','summary':'Summary','gname':'Group','targtype1_txt':'Target_type',
'weaptype1_txt':'Weapon_type','motive':'Motive'},inplace=True )
summary = pd.DataFrame({
'Column Name': df.columns,
'Data Type': df.dtypes,
'Number of Nulls': df.isnull().sum(),
'Percentage of Nulls': (df.isnull().sum() / len(df)) * 100,
'Count of Non-Null Data': df.notnull().sum(),
'number Unique Values': df.nunique()
})
summary['Percentage of Nulls'] = summary['Percentage of Nulls'].map('{:.4f}%'.format)
# # Ensure column names are unique
# summary.columns = [f'{col}_{i}' if summary.columns.tolist().count(col) > 1 else col
# for i, col in enumerate(summary.columns)]
# # Define a function to highlight rows where Number of Nulls > 0
# def highlight_nulls(row):
# return ['background-color: yellow' if row['Number of Nulls'] > 0 else '' for _ in row]
# # Apply the styling to the DataFrame
# styled_summary = summary.style.apply(highlight_nulls, axis=1)
# # Display the styled DataFrame
# styled_summary
summary = summary.reset_index(drop=True)
summary.set_index('Column Name', inplace=True)
summary.T
| Column Name | id | Year | Month | Day | approxdate | extended | resolution | country | Country | region | Region | State | city | latitude | longitude | specificity | vicinity | location | Summary | crit1 | crit2 | crit3 | doubtterr | alternative | alternative_txt | multiple | success | suicide | attacktype1 | AttackType | attacktype2 | attacktype2_txt | attacktype3 | attacktype3_txt | targtype1 | Target_type | targsubtype1 | targsubtype1_txt | corp1 | Target | natlty1 | natlty1_txt | targtype2 | targtype2_txt | targsubtype2 | targsubtype2_txt | corp2 | target2 | natlty2 | natlty2_txt | targtype3 | targtype3_txt | targsubtype3 | targsubtype3_txt | corp3 | target3 | natlty3 | natlty3_txt | Group | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | Motive | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claimmode_txt | claim2 | claimmode2 | claimmode2_txt | claim3 | claimmode3 | claimmode3_txt | compclaim | weaptype1 | Weapon_type | weapsubtype1 | weapsubtype1_txt | weaptype2 | weaptype2_txt | weapsubtype2 | weapsubtype2_txt | weaptype3 | weaptype3_txt | weapsubtype3 | weapsubtype3_txt | weaptype4 | weaptype4_txt | weapsubtype4 | weapsubtype4_txt | weapdetail | Killed | nkillus | nkillter | Wounded | nwoundus | nwoundte | property | propextent | propextent_txt | propvalue | propcomment | ishostkid | nhostkid | nhostkidus | nhours | ndays | divert | kidhijcountry | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | ransomnote | hostkidoutcome | hostkidoutcome_txt | nreleased | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Data Type | int64 | int64 | int64 | int64 | object | int64 | object | int64 | object | int64 | object | object | object | float64 | float64 | float64 | int64 | object | object | int64 | int64 | int64 | float64 | float64 | object | float64 | int64 | int64 | int64 | object | float64 | object | float64 | object | int64 | object | float64 | object | object | object | float64 | object | float64 | object | float64 | object | object | object | float64 | object | float64 | object | float64 | object | object | object | float64 | object | object | object | object | object | object | object | object | float64 | float64 | float64 | int64 | float64 | float64 | float64 | float64 | object | float64 | float64 | object | float64 | float64 | object | float64 | int64 | object | float64 | object | float64 | object | float64 | object | float64 | object | float64 | object | float64 | object | float64 | object | object | float64 | float64 | float64 | float64 | float64 | float64 | int64 | float64 | object | float64 | object | float64 | float64 | float64 | float64 | float64 | object | object | float64 | float64 | float64 | float64 | float64 | object | float64 | object | float64 | object | object | object | object | object | int64 | int64 | int64 | int64 | object |
| Number of Nulls | 0 | 0 | 0 | 0 | 172452 | 0 | 179471 | 0 | 0 | 0 | 0 | 421 | 435 | 4556 | 4557 | 6 | 0 | 126196 | 66129 | 0 | 0 | 0 | 1 | 152680 | 152680 | 1 | 0 | 0 | 0 | 0 | 175377 | 175377 | 181263 | 181263 | 0 | 0 | 10373 | 10373 | 42552 | 638 | 1559 | 1559 | 170547 | 170547 | 171006 | 171006 | 171574 | 170671 | 170863 | 170863 | 180515 | 180515 | 180594 | 180594 | 180665 | 180516 | 180544 | 180544 | 0 | 175801 | 179678 | 181531 | 181367 | 181671 | 131130 | 380 | 179736 | 181371 | 0 | 71115 | 69489 | 66120 | 162608 | 162608 | 179801 | 181075 | 181075 | 181373 | 181558 | 181558 | 176852 | 0 | 0 | 20768 | 20768 | 168564 | 168564 | 170149 | 170149 | 179828 | 179828 | 179998 | 179998 | 181618 | 181618 | 181621 | 181621 | 67670 | 10313 | 64446 | 66958 | 16311 | 64702 | 69143 | 0 | 117626 | 117626 | 142702 | 123732 | 178 | 168119 | 168174 | 177628 | 173567 | 181367 | 178386 | 104310 | 180341 | 181128 | 180917 | 181139 | 181179 | 170700 | 170700 | 171291 | 153402 | 66191 | 104758 | 138175 | 0 | 0 | 0 | 0 | 0 | 156653 |
| Percentage of Nulls | 0.0000% | 0.0000% | 0.0000% | 0.0000% | 94.9150% | 0.0000% | 98.7781% | 0.0000% | 0.0000% | 0.0000% | 0.0000% | 0.2317% | 0.2394% | 2.5076% | 2.5081% | 0.0033% | 0.0000% | 69.4564% | 36.3964% | 0.0000% | 0.0000% | 0.0000% | 0.0006% | 84.0328% | 84.0328% | 0.0006% | 0.0000% | 0.0000% | 0.0000% | 0.0000% | 96.5249% | 96.5249% | 99.7644% | 99.7644% | 0.0000% | 0.0000% | 5.7091% | 5.7091% | 23.4200% | 0.3511% | 0.8581% | 0.8581% | 93.8665% | 93.8665% | 94.1191% | 94.1191% | 94.4318% | 93.9348% | 94.0404% | 94.0404% | 99.3527% | 99.3527% | 99.3962% | 99.3962% | 99.4353% | 99.3533% | 99.3687% | 99.3687% | 0.0000% | 96.7582% | 98.8921% | 99.9119% | 99.8217% | 99.9890% | 72.1720% | 0.2091% | 98.9240% | 99.8239% | 0.0000% | 39.1406% | 38.2457% | 36.3915% | 89.4970% | 89.4970% | 98.9598% | 99.6610% | 99.6610% | 99.8250% | 99.9268% | 99.9268% | 97.3367% | 0.0000% | 0.0000% | 11.4304% | 11.4304% | 92.7751% | 92.7751% | 93.6475% | 93.6475% | 98.9746% | 98.9746% | 99.0682% | 99.0682% | 99.9598% | 99.9598% | 99.9615% | 99.9615% | 37.2446% | 5.6761% | 35.4701% | 36.8527% | 8.9773% | 35.6110% | 38.0553% | 0.0000% | 64.7396% | 64.7396% | 78.5410% | 68.1002% | 0.0980% | 92.5302% | 92.5604% | 97.7638% | 95.5287% | 99.8217% | 98.1810% | 57.4107% | 99.2570% | 99.6901% | 99.5740% | 99.6962% | 99.7182% | 93.9507% | 93.9507% | 94.2760% | 84.4302% | 36.4305% | 57.6572% | 76.0494% | 0.0000% | 0.0000% | 0.0000% | 0.0000% | 0.0000% | 86.2195% |
| Count of Non-Null Data | 181691 | 181691 | 181691 | 181691 | 9239 | 181691 | 2220 | 181691 | 181691 | 181691 | 181691 | 181270 | 181256 | 177135 | 177134 | 181685 | 181691 | 55495 | 115562 | 181691 | 181691 | 181691 | 181690 | 29011 | 29011 | 181690 | 181691 | 181691 | 181691 | 181691 | 6314 | 6314 | 428 | 428 | 181691 | 181691 | 171318 | 171318 | 139139 | 181053 | 180132 | 180132 | 11144 | 11144 | 10685 | 10685 | 10117 | 11020 | 10828 | 10828 | 1176 | 1176 | 1097 | 1097 | 1026 | 1175 | 1147 | 1147 | 181691 | 5890 | 2013 | 160 | 324 | 20 | 50561 | 181311 | 1955 | 320 | 181691 | 110576 | 112202 | 115571 | 19083 | 19083 | 1890 | 616 | 616 | 318 | 133 | 133 | 4839 | 181691 | 181691 | 160923 | 160923 | 13127 | 13127 | 11542 | 11542 | 1863 | 1863 | 1693 | 1693 | 73 | 73 | 70 | 70 | 114021 | 171378 | 117245 | 114733 | 165380 | 116989 | 112548 | 181691 | 64065 | 64065 | 38989 | 57959 | 181513 | 13572 | 13517 | 4063 | 8124 | 324 | 3305 | 77381 | 1350 | 563 | 774 | 552 | 512 | 10991 | 10991 | 10400 | 28289 | 115500 | 76933 | 43516 | 181691 | 181691 | 181691 | 181691 | 181691 | 25038 |
| number Unique Values | 181691 | 47 | 13 | 32 | 2244 | 2 | 1859 | 205 | 205 | 12 | 12 | 2855 | 36673 | 48322 | 48039 | 5 | 3 | 44109 | 112492 | 2 | 2 | 2 | 3 | 5 | 5 | 2 | 2 | 2 | 9 | 9 | 9 | 9 | 8 | 8 | 22 | 22 | 112 | 112 | 33237 | 86005 | 215 | 215 | 22 | 22 | 107 | 107 | 2691 | 5043 | 158 | 158 | 20 | 20 | 92 | 92 | 422 | 720 | 110 | 110 | 3537 | 1183 | 433 | 60 | 116 | 14 | 14490 | 2 | 2 | 2 | 2 | 113 | 50 | 3 | 10 | 10 | 3 | 9 | 9 | 2 | 8 | 8 | 3 | 12 | 12 | 30 | 30 | 11 | 11 | 28 | 28 | 10 | 10 | 22 | 22 | 5 | 5 | 16 | 16 | 19148 | 205 | 31 | 96 | 238 | 44 | 44 | 3 | 4 | 4 | 659 | 19157 | 3 | 209 | 27 | 35 | 328 | 143 | 217 | 3 | 429 | 23 | 156 | 8 | 386 | 7 | 7 | 156 | 15429 | 83988 | 62263 | 36090 | 26 | 3 | 3 | 3 | 3 | 14306 |
# ((df.isna().sum()/len(df))*100).sort_values()
# A plot shows just how much the data is missing values
plt.style.use('default')
missing_values = df.isna().sum()
miss_value = missing_values[missing_values > 0]
# rest_miss = len(df)- miss_value
# rest_miss [miss_value.index]
msno.bar(df[miss_value.index],fontsize=21,sort="ascending",color=sns.color_palette("Reds", len(miss_value)))
plt.title('Missing Values in Dataset ', fontsize=24, weight='bold')
plt.grid(axis='x', linestyle='--', linewidth=0.9, alpha=0.9)
plt.show()
# msno.bar(df,fontsize=21,color="b",sort="descending")
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 181691 entries, 0 to 181690 Columns: 135 entries, id to related dtypes: float64(55), int64(22), object(58) memory usage: 187.1+ MB
df.dtypes.value_counts()
object 58 float64 55 int64 22 Name: count, dtype: int64
df.iloc[:,1:].describe()
| Year | Month | Day | extended | country | region | latitude | longitude | specificity | vicinity | crit1 | crit2 | crit3 | doubtterr | alternative | multiple | success | suicide | attacktype1 | attacktype2 | attacktype3 | targtype1 | targsubtype1 | natlty1 | targtype2 | targsubtype2 | natlty2 | targtype3 | targsubtype3 | natlty3 | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claim2 | claimmode2 | claim3 | claimmode3 | compclaim | weaptype1 | weapsubtype1 | weaptype2 | weapsubtype2 | weaptype3 | weapsubtype3 | weaptype4 | weapsubtype4 | Killed | nkillus | nkillter | Wounded | nwoundus | nwoundte | property | propextent | propvalue | ishostkid | nhostkid | nhostkidus | nhours | ndays | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | hostkidoutcome | nreleased | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 177135.000000 | 1.771340e+05 | 181685.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181690.000000 | 29011.000000 | 181690.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 6314.000000 | 428.000000 | 181691.000000 | 171318.000000 | 180132.000000 | 11144.000000 | 10685.000000 | 10828.000000 | 1176.000000 | 1097.000000 | 1147.000000 | 181311.000000 | 1955.000000 | 320.000000 | 181691.000000 | 110576.000000 | 112202.000000 | 115571.000000 | 19083.000000 | 1890.000000 | 616.000000 | 318.000000 | 133.000000 | 4839.000000 | 181691.000000 | 160923.000000 | 13127.000000 | 11542.000000 | 1863.000000 | 1693.000000 | 73.000000 | 70.000000 | 171378.000000 | 117245.000000 | 114733.000000 | 165380.000000 | 116989.000000 | 112548.000000 | 181691.000000 | 64065.000000 | 3.898900e+04 | 181513.000000 | 13572.000000 | 13517.000000 | 4063.000000 | 8124.000000 | 77381.000000 | 1.350000e+03 | 5.630000e+02 | 7.740000e+02 | 552.000000 | 10991.000000 | 10400.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 |
| mean | 2002.638997 | 6.467277 | 15.505644 | 0.045346 | 131.968501 | 7.160938 | 23.498343 | -4.586957e+02 | 1.451452 | 0.068297 | 0.988530 | 0.993093 | 0.875668 | -0.523171 | 1.292923 | 0.137773 | 0.889598 | 0.036507 | 3.247547 | 3.719512 | 5.245327 | 8.439719 | 46.971474 | 127.686441 | 10.247218 | 55.311652 | 131.179442 | 10.021259 | 55.548769 | 144.564952 | 0.081440 | 0.265473 | 0.193750 | 0.002950 | -65.361154 | -1.517727 | 0.049666 | 7.022848 | 0.247619 | 7.176948 | 0.411950 | 6.729323 | -6.296342 | 6.447325 | 11.117162 | 6.812524 | 10.754029 | 6.911433 | 11.643237 | 6.246575 | 10.842857 | 2.403272 | 0.045981 | 0.508058 | 3.167668 | 0.038944 | 0.107163 | -0.544556 | 3.295403 | 2.088119e+05 | 0.059054 | 4.533230 | -0.353999 | -46.793933 | -32.516371 | -0.145811 | 3.172530e+06 | 5.784865e+05 | 7.179437e+05 | 240.378623 | 4.629242 | -29.018269 | -4.543731 | -4.464398 | 0.090010 | -3.945952 |
| std | 13.259430 | 3.388303 | 8.814045 | 0.208063 | 112.414535 | 2.933408 | 18.569242 | 2.047790e+05 | 0.995430 | 0.284553 | 0.106483 | 0.082823 | 0.329961 | 2.455819 | 0.703729 | 0.344663 | 0.313391 | 0.187549 | 1.915772 | 2.272023 | 2.246642 | 6.653838 | 30.953357 | 89.299120 | 5.709076 | 25.640310 | 125.951485 | 5.723447 | 26.288955 | 163.299295 | 0.273511 | 0.441698 | 0.395854 | 0.054234 | 216.536633 | 12.830346 | 1.093195 | 2.476851 | 0.974018 | 2.783725 | 0.492962 | 2.908003 | 4.234620 | 2.173435 | 6.495612 | 2.277081 | 7.594574 | 2.177956 | 8.493166 | 1.507212 | 8.192672 | 11.545741 | 5.681854 | 4.199937 | 35.949392 | 3.057361 | 1.488881 | 3.122889 | 0.486912 | 1.552463e+07 | 0.461244 | 202.316386 | 6.835645 | 82.800405 | 121.209205 | 1.207861 | 3.021157e+07 | 7.077924e+06 | 1.014392e+07 | 2940.967293 | 2.035360 | 65.720119 | 4.543547 | 4.637152 | 0.568457 | 4.691325 |
| min | 1970.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 | 1.000000 | -53.154613 | -8.618590e+07 | 1.000000 | -9.000000 | 0.000000 | 0.000000 | 0.000000 | -9.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 1.000000 | 1.000000 | 4.000000 | 1.000000 | 1.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | -99.000000 | -9.000000 | 1.000000 | -9.000000 | 1.000000 | 0.000000 | 1.000000 | -9.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 1.000000 | 5.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -9.000000 | 1.000000 | -9.900000e+01 | -9.000000 | -99.000000 | -99.000000 | -99.000000 | -99.000000 | -9.000000 | -9.900000e+01 | -9.900000e+01 | -9.900000e+01 | -99.000000 | 1.000000 | -99.000000 | -9.000000 | -9.000000 | -9.000000 | -9.000000 |
| 25% | 1991.000000 | 4.000000 | 8.000000 | 0.000000 | 78.000000 | 5.000000 | 11.510046 | 4.545640e+00 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 2.000000 | 2.000000 | 3.000000 | 22.000000 | 83.000000 | 4.000000 | 34.000000 | 92.000000 | 3.000000 | 33.000000 | 75.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | 0.000000 | 0.000000 | 6.000000 | 0.000000 | 6.000000 | 0.000000 | 4.000000 | -9.000000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 | 4.000000 | 5.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | -9.900000e+01 | 0.000000 | 1.000000 | 0.000000 | -99.000000 | -99.000000 | 0.000000 | 0.000000e+00 | 0.000000e+00 | -9.900000e+01 | 0.000000 | 2.000000 | -99.000000 | -9.000000 | -9.000000 | 0.000000 | -9.000000 |
| 50% | 2009.000000 | 6.000000 | 15.000000 | 0.000000 | 98.000000 | 6.000000 | 31.467463 | 4.324651e+01 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 2.000000 | 7.000000 | 4.000000 | 35.000000 | 101.000000 | 14.000000 | 67.000000 | 98.000000 | 14.000000 | 67.000000 | 110.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | 0.000000 | 0.000000 | 8.000000 | 0.000000 | 7.000000 | 0.000000 | 7.000000 | -9.000000 | 6.000000 | 12.000000 | 6.000000 | 7.000000 | 6.000000 | 7.000000 | 6.000000 | 9.500000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | -9.900000e+01 | 0.000000 | 2.000000 | 0.000000 | -99.000000 | -99.000000 | 0.000000 | 1.500000e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 4.000000 | 0.000000 | -9.000000 | -9.000000 | 0.000000 | 0.000000 |
| 75% | 2014.000000 | 9.000000 | 23.000000 | 0.000000 | 160.000000 | 10.000000 | 34.685087 | 6.871033e+01 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 7.000000 | 7.000000 | 14.000000 | 74.000000 | 173.000000 | 14.000000 | 69.000000 | 182.000000 | 14.000000 | 73.000000 | 182.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 8.000000 | 1.000000 | 10.000000 | 1.000000 | 9.000000 | 0.000000 | 6.000000 | 16.000000 | 8.000000 | 18.000000 | 9.000000 | 20.000000 | 6.000000 | 16.000000 | 2.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 4.000000 | 1.000000e+03 | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 4.000000e+05 | 0.000000e+00 | 1.273412e+03 | 0.000000 | 7.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 2017.000000 | 12.000000 | 31.000000 | 1.000000 | 1004.000000 | 12.000000 | 74.633553 | 1.793667e+02 | 5.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 5.000000 | 1.000000 | 1.000000 | 1.000000 | 9.000000 | 9.000000 | 8.000000 | 22.000000 | 113.000000 | 1004.000000 | 22.000000 | 113.000000 | 1004.000000 | 22.000000 | 113.000000 | 1004.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 25000.000000 | 406.000000 | 1.000000 | 10.000000 | 1.000000 | 10.000000 | 1.000000 | 10.000000 | 1.000000 | 13.000000 | 31.000000 | 13.000000 | 31.000000 | 13.000000 | 28.000000 | 12.000000 | 28.000000 | 1570.000000 | 1360.000000 | 500.000000 | 8191.000000 | 751.000000 | 200.000000 | 1.000000 | 4.000000 | 2.700000e+09 | 1.000000 | 17000.000000 | 86.000000 | 999.000000 | 2454.000000 | 1.000000 | 1.000000e+09 | 1.320000e+08 | 2.750000e+08 | 48000.000000 | 7.000000 | 2769.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
missing_values = df.isna().sum()
miss_value_prec = (missing_values[missing_values > 0]/len(df))*100
miss_value = missing_values[missing_values > 0]
miss_value
approxdate 172452 resolution 179471 State 421 city 435 latitude 4556 longitude 4557 specificity 6 location 126196 Summary 66129 doubtterr 1 alternative 152680 alternative_txt 152680 multiple 1 attacktype2 175377 attacktype2_txt 175377 attacktype3 181263 attacktype3_txt 181263 targsubtype1 10373 targsubtype1_txt 10373 corp1 42552 Target 638 natlty1 1559 natlty1_txt 1559 targtype2 170547 targtype2_txt 170547 targsubtype2 171006 targsubtype2_txt 171006 corp2 171574 target2 170671 natlty2 170863 natlty2_txt 170863 targtype3 180515 targtype3_txt 180515 targsubtype3 180594 targsubtype3_txt 180594 corp3 180665 target3 180516 natlty3 180544 natlty3_txt 180544 gsubname 175801 gname2 179678 gsubname2 181531 gname3 181367 gsubname3 181671 Motive 131130 guncertain1 380 guncertain2 179736 guncertain3 181371 nperps 71115 nperpcap 69489 claimed 66120 claimmode 162608 claimmode_txt 162608 claim2 179801 claimmode2 181075 claimmode2_txt 181075 claim3 181373 claimmode3 181558 claimmode3_txt 181558 compclaim 176852 weapsubtype1 20768 weapsubtype1_txt 20768 weaptype2 168564 weaptype2_txt 168564 weapsubtype2 170149 weapsubtype2_txt 170149 weaptype3 179828 weaptype3_txt 179828 weapsubtype3 179998 weapsubtype3_txt 179998 weaptype4 181618 weaptype4_txt 181618 weapsubtype4 181621 weapsubtype4_txt 181621 weapdetail 67670 Killed 10313 nkillus 64446 nkillter 66958 Wounded 16311 nwoundus 64702 nwoundte 69143 propextent 117626 propextent_txt 117626 propvalue 142702 propcomment 123732 ishostkid 178 nhostkid 168119 nhostkidus 168174 nhours 177628 ndays 173567 divert 181367 kidhijcountry 178386 ransom 104310 ransomamt 180341 ransomamtus 181128 ransompaid 180917 ransompaidus 181139 ransomnote 181179 hostkidoutcome 170700 hostkidoutcome_txt 170700 nreleased 171291 addnotes 153402 scite1 66191 scite2 104758 scite3 138175 related 156653 dtype: int64
# features contain nulls
indx_na = df.isna().sum()
indx_na[indx_na >0].index.to_list()
['approxdate', 'resolution', 'State', 'city', 'latitude', 'longitude', 'specificity', 'location', 'Summary', 'doubtterr', 'alternative', 'alternative_txt', 'multiple', 'attacktype2', 'attacktype2_txt', 'attacktype3', 'attacktype3_txt', 'targsubtype1', 'targsubtype1_txt', 'corp1', 'Target', 'natlty1', 'natlty1_txt', 'targtype2', 'targtype2_txt', 'targsubtype2', 'targsubtype2_txt', 'corp2', 'target2', 'natlty2', 'natlty2_txt', 'targtype3', 'targtype3_txt', 'targsubtype3', 'targsubtype3_txt', 'corp3', 'target3', 'natlty3', 'natlty3_txt', 'gsubname', 'gname2', 'gsubname2', 'gname3', 'gsubname3', 'Motive', 'guncertain1', 'guncertain2', 'guncertain3', 'nperps', 'nperpcap', 'claimed', 'claimmode', 'claimmode_txt', 'claim2', 'claimmode2', 'claimmode2_txt', 'claim3', 'claimmode3', 'claimmode3_txt', 'compclaim', 'weapsubtype1', 'weapsubtype1_txt', 'weaptype2', 'weaptype2_txt', 'weapsubtype2', 'weapsubtype2_txt', 'weaptype3', 'weaptype3_txt', 'weapsubtype3', 'weapsubtype3_txt', 'weaptype4', 'weaptype4_txt', 'weapsubtype4', 'weapsubtype4_txt', 'weapdetail', 'Killed', 'nkillus', 'nkillter', 'Wounded', 'nwoundus', 'nwoundte', 'propextent', 'propextent_txt', 'propvalue', 'propcomment', 'ishostkid', 'nhostkid', 'nhostkidus', 'nhours', 'ndays', 'divert', 'kidhijcountry', 'ransom', 'ransomamt', 'ransomamtus', 'ransompaid', 'ransompaidus', 'ransomnote', 'hostkidoutcome', 'hostkidoutcome_txt', 'nreleased', 'addnotes', 'scite1', 'scite2', 'scite3', 'related']
## features not contain nulls
indx_notna = df.notna().sum()
indx_notna[indx_notna == len(df)].index.to_list()
['id', 'Year', 'Month', 'Day', 'extended', 'country', 'Country', 'region', 'Region', 'vicinity', 'crit1', 'crit2', 'crit3', 'success', 'suicide', 'attacktype1', 'AttackType', 'targtype1', 'Target_type', 'Group', 'individual', 'weaptype1', 'Weapon_type', 'property', 'dbsource', 'INT_LOG', 'INT_IDEO', 'INT_MISC', 'INT_ANY']
df['resolution'] = pd.to_datetime(df['resolution'], format='%m/%d/%Y')
# only numeric columns.
numeric_cols = df.select_dtypes(include=[np.number])
numeric_cols
| id | Year | Month | Day | extended | country | region | latitude | longitude | specificity | vicinity | crit1 | crit2 | crit3 | doubtterr | alternative | multiple | success | suicide | attacktype1 | attacktype2 | attacktype3 | targtype1 | targsubtype1 | natlty1 | targtype2 | targsubtype2 | natlty2 | targtype3 | targsubtype3 | natlty3 | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claim2 | claimmode2 | claim3 | claimmode3 | compclaim | weaptype1 | weapsubtype1 | weaptype2 | weapsubtype2 | weaptype3 | weapsubtype3 | weaptype4 | weapsubtype4 | Killed | nkillus | nkillter | Wounded | nwoundus | nwoundte | property | propextent | propvalue | ishostkid | nhostkid | nhostkidus | nhours | ndays | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | hostkidoutcome | nreleased | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 197000000001 | 1970 | 7 | 2 | 0 | 58 | 2 | 18.456792 | -69.951164 | 1.0 | 0 | 1 | 1 | 1 | 0.0 | NaN | 0.0 | 1 | 0 | 1 | NaN | NaN | 14 | 68.0 | 58.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 0 | 0 | 0 |
| 1 | 197000000002 | 1970 | 0 | 0 | 0 | 130 | 1 | 19.371887 | -99.086624 | 1.0 | 0 | 1 | 1 | 1 | 0.0 | NaN | 0.0 | 1 | 0 | 6 | NaN | NaN | 7 | 45.0 | 21.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | 7.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | 1.0 | 1.0 | 0.0 | NaN | NaN | 1.0 | 800000.0 | NaN | NaN | NaN | NaN | NaN | 0 | 1 | 1 | 1 |
| 2 | 197001000001 | 1970 | 1 | 0 | 0 | 160 | 5 | 15.478598 | 120.599741 | 4.0 | 0 | 1 | 1 | 1 | 0.0 | NaN | 0.0 | 1 | 0 | 1 | NaN | NaN | 10 | 54.0 | 217.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | -9 | -9 | 1 | 1 |
| 3 | 197001000002 | 1970 | 1 | 0 | 0 | 78 | 8 | 37.997490 | 23.762728 | 1.0 | 0 | 1 | 1 | 1 | 0.0 | NaN | 0.0 | 1 | 0 | 3 | NaN | NaN | 7 | 46.0 | 217.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | 16.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | -9 | -9 | 1 | 1 |
| 4 | 197001000003 | 1970 | 1 | 0 | 0 | 101 | 4 | 33.580412 | 130.396361 | 1.0 | 0 | 1 | 1 | 1 | -9.0 | NaN | 0.0 | 1 | 0 | 7 | NaN | NaN | 7 | 46.0 | 217.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | -9 | -9 | 1 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 181686 | 201712310022 | 2017 | 12 | 31 | 0 | 182 | 11 | 2.359673 | 45.385034 | 2.0 | 0 | 1 | 1 | 0 | 1.0 | 1.0 | 0.0 | 1 | 0 | 2 | NaN | NaN | 4 | 36.0 | 182.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 1.0 | 10.0 | NaN | NaN | NaN | NaN | NaN | 5 | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | -9 | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 0 | 0 | 0 |
| 181687 | 201712310029 | 2017 | 12 | 31 | 0 | 200 | 10 | 35.407278 | 35.942679 | 1.0 | 1 | 1 | 1 | 0 | 1.0 | 1.0 | 0.0 | 1 | 0 | 3 | NaN | NaN | 4 | 27.0 | 167.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 6 | 11.0 | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 0.0 | 7.0 | 0.0 | 0.0 | 1 | 4.0 | -99.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -9 | -9 | 1 | 1 |
| 181688 | 201712310030 | 2017 | 12 | 31 | 0 | 160 | 5 | 6.900742 | 124.437908 | 2.0 | 0 | 1 | 1 | 1 | 0.0 | NaN | 0.0 | 1 | 0 | 7 | NaN | NaN | 14 | 76.0 | 160.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 8 | 18.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 4.0 | -99.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 0 | 0 | 0 |
| 181689 | 201712310031 | 2017 | 12 | 31 | 0 | 92 | 6 | 24.798346 | 93.940430 | 1.0 | 0 | 1 | 1 | 1 | 0.0 | NaN | 0.0 | 0 | 0 | 3 | NaN | NaN | 2 | 21.0 | 92.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 6 | 7.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -9 | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -9 | -9 | 0 | -9 |
| 181690 | 201712310032 | 2017 | 12 | 31 | 0 | 160 | 5 | 7.209594 | 124.241966 | 1.0 | 0 | 1 | 1 | 1 | 0.0 | NaN | 0.0 | 0 | 0 | 3 | NaN | NaN | 20 | NaN | 160.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 6 | 16.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -9 | -9 | 0 | -9 |
181691 rows × 77 columns
# Calculate the mean, median, and standard deviation of relevant numeric columns.
numeric_cols = df.select_dtypes(include=[np.number])
numeric_summary = pd.DataFrame({
'Mean': numeric_cols.mean(),
'Median': numeric_cols.median(),
'Standard Deviation': numeric_cols.std()
})
numeric_summary = numeric_summary.reset_index().rename(columns={'index': 'Column Name'})[1:].set_index('Column Name').T
numeric_summary
| Column Name | Year | Month | Day | extended | country | region | latitude | longitude | specificity | vicinity | crit1 | crit2 | crit3 | doubtterr | alternative | multiple | success | suicide | attacktype1 | attacktype2 | attacktype3 | targtype1 | targsubtype1 | natlty1 | targtype2 | targsubtype2 | natlty2 | targtype3 | targsubtype3 | natlty3 | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claim2 | claimmode2 | claim3 | claimmode3 | compclaim | weaptype1 | weapsubtype1 | weaptype2 | weapsubtype2 | weaptype3 | weapsubtype3 | weaptype4 | weapsubtype4 | Killed | nkillus | nkillter | Wounded | nwoundus | nwoundte | property | propextent | propvalue | ishostkid | nhostkid | nhostkidus | nhours | ndays | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | hostkidoutcome | nreleased | INT_LOG | INT_IDEO | INT_MISC | INT_ANY |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | 2002.638997 | 6.467277 | 15.505644 | 0.045346 | 131.968501 | 7.160938 | 23.498343 | -458.695653 | 1.451452 | 0.068297 | 0.988530 | 0.993093 | 0.875668 | -0.523171 | 1.292923 | 0.137773 | 0.889598 | 0.036507 | 3.247547 | 3.719512 | 5.245327 | 8.439719 | 46.971474 | 127.686441 | 10.247218 | 55.311652 | 131.179442 | 10.021259 | 55.548769 | 144.564952 | 0.081440 | 0.265473 | 0.193750 | 0.002950 | -65.361154 | -1.517727 | 0.049666 | 7.022848 | 0.247619 | 7.176948 | 0.411950 | 6.729323 | -6.296342 | 6.447325 | 11.117162 | 6.812524 | 10.754029 | 6.911433 | 11.643237 | 6.246575 | 10.842857 | 2.403272 | 0.045981 | 0.508058 | 3.167668 | 0.038944 | 0.107163 | -0.544556 | 3.295403 | 2.088119e+05 | 0.059054 | 4.533230 | -0.353999 | -46.793933 | -32.516371 | -0.145811 | 3.172530e+06 | 5.784865e+05 | 7.179437e+05 | 240.378623 | 4.629242 | -29.018269 | -4.543731 | -4.464398 | 0.090010 | -3.945952 |
| Median | 2009.000000 | 6.000000 | 15.000000 | 0.000000 | 98.000000 | 6.000000 | 31.467463 | 43.246506 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 2.000000 | 7.000000 | 4.000000 | 35.000000 | 101.000000 | 14.000000 | 67.000000 | 98.000000 | 14.000000 | 67.000000 | 110.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | 0.000000 | 0.000000 | 8.000000 | 0.000000 | 7.000000 | 0.000000 | 7.000000 | -9.000000 | 6.000000 | 12.000000 | 6.000000 | 7.000000 | 6.000000 | 7.000000 | 6.000000 | 9.500000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | -9.900000e+01 | 0.000000 | 2.000000 | 0.000000 | -99.000000 | -99.000000 | 0.000000 | 1.500000e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 4.000000 | 0.000000 | -9.000000 | -9.000000 | 0.000000 | 0.000000 |
| Standard Deviation | 13.259430 | 3.388303 | 8.814045 | 0.208063 | 112.414535 | 2.933408 | 18.569242 | 204778.988611 | 0.995430 | 0.284553 | 0.106483 | 0.082823 | 0.329961 | 2.455819 | 0.703729 | 0.344663 | 0.313391 | 0.187549 | 1.915772 | 2.272023 | 2.246642 | 6.653838 | 30.953357 | 89.299120 | 5.709076 | 25.640310 | 125.951485 | 5.723447 | 26.288955 | 163.299295 | 0.273511 | 0.441698 | 0.395854 | 0.054234 | 216.536633 | 12.830346 | 1.093195 | 2.476851 | 0.974018 | 2.783725 | 0.492962 | 2.908003 | 4.234620 | 2.173435 | 6.495612 | 2.277081 | 7.594574 | 2.177956 | 8.493166 | 1.507212 | 8.192672 | 11.545741 | 5.681854 | 4.199937 | 35.949392 | 3.057361 | 1.488881 | 3.122889 | 0.486912 | 1.552463e+07 | 0.461244 | 202.316386 | 6.835645 | 82.800405 | 121.209205 | 1.207861 | 3.021157e+07 | 7.077924e+06 | 1.014392e+07 | 2940.967293 | 2.035360 | 65.720119 | 4.543547 | 4.637152 | 0.568457 | 4.691325 |
# most frequent values in categorical columns.
catego_cols = df.select_dtypes(include=['object'])
catego_cols
| approxdate | Country | Region | State | city | location | Summary | alternative_txt | AttackType | attacktype2_txt | attacktype3_txt | Target_type | targsubtype1_txt | corp1 | Target | natlty1_txt | targtype2_txt | targsubtype2_txt | corp2 | target2 | natlty2_txt | targtype3_txt | targsubtype3_txt | corp3 | target3 | natlty3_txt | Group | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | Motive | claimmode_txt | claimmode2_txt | claimmode3_txt | Weapon_type | weapsubtype1_txt | weaptype2_txt | weapsubtype2_txt | weaptype3_txt | weapsubtype3_txt | weaptype4_txt | weapsubtype4_txt | weapdetail | propextent_txt | propcomment | divert | kidhijcountry | ransomnote | hostkidoutcome_txt | addnotes | scite1 | scite2 | scite3 | dbsource | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Dominican Republic | Central America & Caribbean | NaN | Santo Domingo | NaN | NaN | NaN | Assassination | NaN | NaN | Private Citizens & Property | Named Civilian | NaN | Julio Guzman | Dominican Republic | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | MANO-D | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | NaN |
| 1 | NaN | Mexico | North America | Federal | Mexico city | NaN | NaN | NaN | Hostage Taking (Kidnapping) | NaN | NaN | Government (Diplomatic) | Diplomatic Personnel (outside of embassy, cons... | Belgian Ambassador Daughter | Nadine Chaval, daughter | Belgium | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23rd of September Communist League | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Mexico | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | NaN |
| 2 | NaN | Philippines | Southeast Asia | Tarlac | Unknown | NaN | NaN | NaN | Assassination | NaN | NaN | Journalists & Media | Radio Journalist/Staff/Facility | Voice of America | Employee | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | NaN |
| 3 | NaN | Greece | Western Europe | Attica | Athens | NaN | NaN | NaN | Bombing/Explosion | NaN | NaN | Government (Diplomatic) | Embassy/Consulate | NaN | U.S. Embassy | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosives | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | Explosive | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | NaN |
| 4 | NaN | Japan | East Asia | Fukouka | Fukouka | NaN | NaN | NaN | Facility/Infrastructure Attack | NaN | NaN | Government (Diplomatic) | Embassy/Consulate | NaN | U.S. Consulate | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 181686 | NaN | Somalia | Sub-Saharan Africa | Middle Shebelle | Ceelka Geelow | The incident occurred near the town of Balcad. | 12/31/2017: Assailants opened fire on a Somali... | Insurgency/Guerilla Action | Armed Assault | NaN | NaN | Military | Military Checkpoint | Somali National Army (SNA) | Checkpoint | Somalia | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Al-Shabaab | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | Firearms | Unknown Gun Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Somalia: Al-Shabaab Militants Attack Army Che... | "Highlights: Somalia Daily Media Highlights 2 ... | "Highlights: Somalia Daily Media Highlights 1 ... | START Primary Collection | NaN |
| 181687 | NaN | Syria | Middle East & North Africa | Lattakia | Jableh | The incident occurred at the Humaymim Airport. | 12/31/2017: Assailants launched mortars at the... | Insurgency/Guerilla Action | Bombing/Explosion | NaN | NaN | Military | Military Barracks/Base/Headquarters/Checkpost | Russian Air Force | Hmeymim Air Base | Russia | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Muslim extremists | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosives | Projectile (rockets, mortars, RPGs, etc.) | NaN | NaN | NaN | NaN | NaN | NaN | Mortars were used in the attack. | Unknown | Seven military planes were damaged in this att... | NaN | NaN | NaN | NaN | NaN | "Putin's 'victory' in Syria has turned into a ... | "Two Russian soldiers killed at Hmeymim base i... | "Two Russian servicemen killed in Syria mortar... | START Primary Collection | NaN |
| 181688 | NaN | Philippines | Southeast Asia | Maguindanao | Kubentog | The incident occurred in the Datu Hoffer distr... | 12/31/2017: Assailants set fire to houses in K... | NaN | Facility/Infrastructure Attack | NaN | NaN | Private Citizens & Property | House/Apartment/Residence | Not Applicable | Houses | Philippines | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Bangsamoro Islamic Freedom Movement (BIFM) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Incendiary | Arson/Fire | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | Houses were damaged in this attack. | NaN | NaN | NaN | NaN | NaN | "Maguindanao clashes trap tribe members," Phil... | NaN | NaN | START Primary Collection | NaN |
| 181689 | NaN | India | South Asia | Manipur | Imphal | The incident occurred in the Mantripukhri neig... | 12/31/2017: Assailants threw a grenade at a Fo... | NaN | Bombing/Explosion | NaN | NaN | Government (General) | Government Building/Facility/Office | Forest Department Manipur | Office | India | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosives | Grenade | NaN | NaN | NaN | NaN | NaN | NaN | A thrown grenade was used in the attack. | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Trader escapes grenade attack in Imphal," Bus... | NaN | NaN | START Primary Collection | NaN |
| 181690 | NaN | Philippines | Southeast Asia | Maguindanao | Cotabato City | NaN | 12/31/2017: An explosive device was discovered... | NaN | Bombing/Explosion | NaN | NaN | Unknown | NaN | Unknown | Unknown | Philippines | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosives | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | An explosive device containing a detonating co... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Security tightened in Cotabato following IED ... | "Security tightened in Cotabato City," Manila ... | NaN | START Primary Collection | NaN |
181691 rows × 57 columns
# Find the most frequent value (mode) for each categorical column
most_frequent = catego_cols.mode()
most_frequent.index =["most_freq_"]
most_frequent
| approxdate | Country | Region | State | city | location | Summary | alternative_txt | AttackType | attacktype2_txt | attacktype3_txt | Target_type | targsubtype1_txt | corp1 | Target | natlty1_txt | targtype2_txt | targsubtype2_txt | corp2 | target2 | natlty2_txt | targtype3_txt | targsubtype3_txt | corp3 | target3 | natlty3_txt | Group | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | Motive | claimmode_txt | claimmode2_txt | claimmode3_txt | Weapon_type | weapsubtype1_txt | weaptype2_txt | weapsubtype2_txt | weaptype3_txt | weapsubtype3_txt | weaptype4_txt | weapsubtype4_txt | weapdetail | propextent_txt | propcomment | divert | kidhijcountry | ransomnote | hostkidoutcome_txt | addnotes | scite1 | scite2 | scite3 | dbsource | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| most_freq_ | September 18-24, 2016 | Iraq | Middle East & North Africa | Baghdad | Unknown | The attack took place in Baghdad, Baghdad, Iraq. | 09/00/2016: Sometime between September 18, 201... | Insurgency/Guerilla Action | Bombing/Explosion | Armed Assault | Facility/Infrastructure Attack | Private Citizens & Property | Unnamed Civilian/Unspecified | Unknown | Civilians | Iraq | Private Citizens & Property | Unnamed Civilian/Unspecified | Not Applicable | Civilians | Iraq | Private Citizens & Property | Unnamed Civilian/Unspecified | Not Applicable | Civilians | Iraq | Unknown | Militants | Al-Nusrah Front | The Family | Free Syrian Army | Jaysh al-Nukhba; Al-Izzah | Unknown | Personal claim | Unknown | Unknown | Explosives | Unknown Explosive Type | Firearms | Unknown Gun Type | Firearms | Unknown Gun Type | Firearms | Automatic or Semi-Automatic Rifle | Explosive | Minor (likely < $1 million) | It is unknown if any property was damaged in t... | Unknown | Colombia | 0 | Unknown | Casualty numbers for this incident conflict ac... | Committee on Government Operations United Stat... | Christopher Hewitt, "Political Violence and Te... | Christopher Hewitt, "Political Violence and Te... | START Primary Collection | 201612010023, 201612010024, 201612010025, 2016... |
Given the extensive number of columns in the dataset, we'll focus on selecting only the key columns for data preprocessing to ensure a more efficient and manageable analysis. By concentrating on the most relevant columns, we can streamline our efforts and derive meaningful insights from the dataset.
#As we have many columns, we take the columns that are necessary for analysis.
df_terr = df[['id','Year','Month','Day','Country','Region','State','city','latitude','longitude','AttackType','Killed','Wounded',
'Target','Summary','Group','Target_type','Weapon_type','Motive','success']]
df_terr.head()
| id | Year | Month | Day | Country | Region | State | city | latitude | longitude | AttackType | Killed | Wounded | Target | Summary | Group | Target_type | Weapon_type | Motive | success | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 197000000001 | 1970 | 7 | 2 | Dominican Republic | Central America & Caribbean | NaN | Santo Domingo | 18.456792 | -69.951164 | Assassination | 1.0 | 0.0 | Julio Guzman | NaN | MANO-D | Private Citizens & Property | Unknown | NaN | 1 |
| 1 | 197000000002 | 1970 | 0 | 0 | Mexico | North America | Federal | Mexico city | 19.371887 | -99.086624 | Hostage Taking (Kidnapping) | 0.0 | 0.0 | Nadine Chaval, daughter | NaN | 23rd of September Communist League | Government (Diplomatic) | Unknown | NaN | 1 |
| 2 | 197001000001 | 1970 | 1 | 0 | Philippines | Southeast Asia | Tarlac | Unknown | 15.478598 | 120.599741 | Assassination | 1.0 | 0.0 | Employee | NaN | Unknown | Journalists & Media | Unknown | NaN | 1 |
| 3 | 197001000002 | 1970 | 1 | 0 | Greece | Western Europe | Attica | Athens | 37.997490 | 23.762728 | Bombing/Explosion | NaN | NaN | U.S. Embassy | NaN | Unknown | Government (Diplomatic) | Explosives | NaN | 1 |
| 4 | 197001000003 | 1970 | 1 | 0 | Japan | East Asia | Fukouka | Fukouka | 33.580412 | 130.396361 | Facility/Infrastructure Attack | NaN | NaN | U.S. Consulate | NaN | Unknown | Government (Diplomatic) | Incendiary | NaN | 1 |
# to get on overall casualties killing and wounding.
df_terr['casualties'] = df_terr['Killed'].fillna(0) + df_terr['Wounded'].fillna(0)
df_terr.shape
(181691, 21)
df_terr.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 181691 entries, 0 to 181690 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 181691 non-null int64 1 Year 181691 non-null int64 2 Month 181691 non-null int64 3 Day 181691 non-null int64 4 Country 181691 non-null object 5 Region 181691 non-null object 6 State 181270 non-null object 7 city 181256 non-null object 8 latitude 177135 non-null float64 9 longitude 177134 non-null float64 10 AttackType 181691 non-null object 11 Killed 171378 non-null float64 12 Wounded 165380 non-null float64 13 Target 181053 non-null object 14 Summary 115562 non-null object 15 Group 181691 non-null object 16 Target_type 181691 non-null object 17 Weapon_type 181691 non-null object 18 Motive 50561 non-null object 19 success 181691 non-null int64 20 casualties 181691 non-null float64 dtypes: float64(5), int64(5), object(11) memory usage: 29.1+ MB
# Calculate the mean, median, and standard deviation of relevant numeric columns -- on new dataset (df_terr).
num_cols = df_terr.select_dtypes(include=[np.number])
num_summary = pd.DataFrame({
'Mean': numeric_cols.mean(),
'Median': numeric_cols.median(),
'Standard Deviation': numeric_cols.std()
})
numeric_view = num_summary.reset_index().rename(columns={'index': 'Column Name'})[1:].set_index('Column Name').T
numeric_view
| Column Name | Year | Month | Day | extended | country | region | latitude | longitude | specificity | vicinity | crit1 | crit2 | crit3 | doubtterr | alternative | multiple | success | suicide | attacktype1 | attacktype2 | attacktype3 | targtype1 | targsubtype1 | natlty1 | targtype2 | targsubtype2 | natlty2 | targtype3 | targsubtype3 | natlty3 | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claim2 | claimmode2 | claim3 | claimmode3 | compclaim | weaptype1 | weapsubtype1 | weaptype2 | weapsubtype2 | weaptype3 | weapsubtype3 | weaptype4 | weapsubtype4 | Killed | nkillus | nkillter | Wounded | nwoundus | nwoundte | property | propextent | propvalue | ishostkid | nhostkid | nhostkidus | nhours | ndays | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | hostkidoutcome | nreleased | INT_LOG | INT_IDEO | INT_MISC | INT_ANY |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | 2002.638997 | 6.467277 | 15.505644 | 0.045346 | 131.968501 | 7.160938 | 23.498343 | -458.695653 | 1.451452 | 0.068297 | 0.988530 | 0.993093 | 0.875668 | -0.523171 | 1.292923 | 0.137773 | 0.889598 | 0.036507 | 3.247547 | 3.719512 | 5.245327 | 8.439719 | 46.971474 | 127.686441 | 10.247218 | 55.311652 | 131.179442 | 10.021259 | 55.548769 | 144.564952 | 0.081440 | 0.265473 | 0.193750 | 0.002950 | -65.361154 | -1.517727 | 0.049666 | 7.022848 | 0.247619 | 7.176948 | 0.411950 | 6.729323 | -6.296342 | 6.447325 | 11.117162 | 6.812524 | 10.754029 | 6.911433 | 11.643237 | 6.246575 | 10.842857 | 2.403272 | 0.045981 | 0.508058 | 3.167668 | 0.038944 | 0.107163 | -0.544556 | 3.295403 | 2.088119e+05 | 0.059054 | 4.533230 | -0.353999 | -46.793933 | -32.516371 | -0.145811 | 3.172530e+06 | 5.784865e+05 | 7.179437e+05 | 240.378623 | 4.629242 | -29.018269 | -4.543731 | -4.464398 | 0.090010 | -3.945952 |
| Median | 2009.000000 | 6.000000 | 15.000000 | 0.000000 | 98.000000 | 6.000000 | 31.467463 | 43.246506 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 2.000000 | 7.000000 | 4.000000 | 35.000000 | 101.000000 | 14.000000 | 67.000000 | 98.000000 | 14.000000 | 67.000000 | 110.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | 0.000000 | 0.000000 | 8.000000 | 0.000000 | 7.000000 | 0.000000 | 7.000000 | -9.000000 | 6.000000 | 12.000000 | 6.000000 | 7.000000 | 6.000000 | 7.000000 | 6.000000 | 9.500000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | -9.900000e+01 | 0.000000 | 2.000000 | 0.000000 | -99.000000 | -99.000000 | 0.000000 | 1.500000e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 4.000000 | 0.000000 | -9.000000 | -9.000000 | 0.000000 | 0.000000 |
| Standard Deviation | 13.259430 | 3.388303 | 8.814045 | 0.208063 | 112.414535 | 2.933408 | 18.569242 | 204778.988611 | 0.995430 | 0.284553 | 0.106483 | 0.082823 | 0.329961 | 2.455819 | 0.703729 | 0.344663 | 0.313391 | 0.187549 | 1.915772 | 2.272023 | 2.246642 | 6.653838 | 30.953357 | 89.299120 | 5.709076 | 25.640310 | 125.951485 | 5.723447 | 26.288955 | 163.299295 | 0.273511 | 0.441698 | 0.395854 | 0.054234 | 216.536633 | 12.830346 | 1.093195 | 2.476851 | 0.974018 | 2.783725 | 0.492962 | 2.908003 | 4.234620 | 2.173435 | 6.495612 | 2.277081 | 7.594574 | 2.177956 | 8.493166 | 1.507212 | 8.192672 | 11.545741 | 5.681854 | 4.199937 | 35.949392 | 3.057361 | 1.488881 | 3.122889 | 0.486912 | 1.552463e+07 | 0.461244 | 202.316386 | 6.835645 | 82.800405 | 121.209205 | 1.207861 | 3.021157e+07 | 7.077924e+06 | 1.014392e+07 | 2940.967293 | 2.035360 | 65.720119 | 4.543547 | 4.637152 | 0.568457 | 4.691325 |
# Find the most frequent value (mode) for each categorical column
most_freq = catego_cols.mode()
most_freq.index =["most_freq_"]
most_freq
| approxdate | Country | Region | State | city | location | Summary | alternative_txt | AttackType | attacktype2_txt | attacktype3_txt | Target_type | targsubtype1_txt | corp1 | Target | natlty1_txt | targtype2_txt | targsubtype2_txt | corp2 | target2 | natlty2_txt | targtype3_txt | targsubtype3_txt | corp3 | target3 | natlty3_txt | Group | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | Motive | claimmode_txt | claimmode2_txt | claimmode3_txt | Weapon_type | weapsubtype1_txt | weaptype2_txt | weapsubtype2_txt | weaptype3_txt | weapsubtype3_txt | weaptype4_txt | weapsubtype4_txt | weapdetail | propextent_txt | propcomment | divert | kidhijcountry | ransomnote | hostkidoutcome_txt | addnotes | scite1 | scite2 | scite3 | dbsource | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| most_freq_ | September 18-24, 2016 | Iraq | Middle East & North Africa | Baghdad | Unknown | The attack took place in Baghdad, Baghdad, Iraq. | 09/00/2016: Sometime between September 18, 201... | Insurgency/Guerilla Action | Bombing/Explosion | Armed Assault | Facility/Infrastructure Attack | Private Citizens & Property | Unnamed Civilian/Unspecified | Unknown | Civilians | Iraq | Private Citizens & Property | Unnamed Civilian/Unspecified | Not Applicable | Civilians | Iraq | Private Citizens & Property | Unnamed Civilian/Unspecified | Not Applicable | Civilians | Iraq | Unknown | Militants | Al-Nusrah Front | The Family | Free Syrian Army | Jaysh al-Nukhba; Al-Izzah | Unknown | Personal claim | Unknown | Unknown | Explosives | Unknown Explosive Type | Firearms | Unknown Gun Type | Firearms | Unknown Gun Type | Firearms | Automatic or Semi-Automatic Rifle | Explosive | Minor (likely < $1 million) | It is unknown if any property was damaged in t... | Unknown | Colombia | 0 | Unknown | Casualty numbers for this incident conflict ac... | Committee on Government Operations United Stat... | Christopher Hewitt, "Political Violence and Te... | Christopher Hewitt, "Political Violence and Te... | START Primary Collection | 201612010023, 201612010024, 201612010025, 2016... |
df_terr.iloc[:,1:].corr(numeric_only=True) # correlation between numerical features
| Year | Month | Day | latitude | longitude | Killed | Wounded | success | casualties | |
|---|---|---|---|---|---|---|---|---|---|
| Year | 1.000000 | 0.000139 | 0.018254 | 0.166933 | 0.003917 | 0.015341 | 0.015273 | -0.082963 | 0.020675 |
| Month | 0.000139 | 1.000000 | 0.005497 | -0.015978 | -0.003880 | 0.003463 | 0.002938 | -0.002845 | 0.003805 |
| Day | 0.018254 | 0.005497 | 1.000000 | 0.003423 | -0.002285 | -0.003693 | -0.001268 | -0.011802 | -0.001808 |
| latitude | 0.166933 | -0.015978 | 0.003423 | 1.000000 | 0.001463 | -0.018124 | 0.015988 | -0.073715 | 0.009899 |
| longitude | 0.003917 | -0.003880 | -0.002285 | 0.001463 | 1.000000 | -0.000562 | 0.000223 | -0.000858 | 0.000013 |
| Killed | 0.015341 | 0.003463 | -0.003693 | -0.018124 | -0.000562 | 1.000000 | 0.534375 | 0.053115 | 0.651615 |
| Wounded | 0.015273 | 0.002938 | -0.001268 | 0.015988 | 0.000223 | 0.534375 | 1.000000 | 0.025804 | 0.980386 |
| success | -0.082963 | -0.002845 | -0.011802 | -0.073715 | -0.000858 | 0.053115 | 0.025804 | 1.000000 | 0.033487 |
| casualties | 0.020675 | 0.003805 | -0.001808 | 0.009899 | 0.000013 | 0.651615 | 0.980386 | 0.033487 | 1.000000 |
#Heatmap to visualize the correlation between numerical features
import pandas as pd
import seaborn as sns
import matplotlib.pyplot
correlation_matrix = df_terr.iloc[:,1:].corr(numeric_only=True)
# correlation_matrix=num_cols.corr()
plt.style.use('default')
# Create the heatmap
plt.figure(figsize=(14, 10))
sns.heatmap(correlation_matrix, annot=True, cmap=sns.mpl_palette("viridis_r", as_cmap=True), fmt='.2f', linewidths=0.5, vmin=-1, vmax=1)
# Add labels and title
plt.title('Correlation Heatmap of Features', fontsize=16, fontweight='bold')
plt.xlabel('Features', fontsize=14)
plt.ylabel('Features', fontsize=14)
# Show the plot
plt.show()
# number terrsiom attacks per year
attacks_per_year = df_terr.groupby('Year').size().reset_index(name='number of attacks per year')
attacks_per_year.T
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | 1978 | 1979 | 1980 | 1981 | 1982 | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
| number of attacks per year | 651 | 471 | 568 | 473 | 581 | 740 | 923 | 1319 | 1526 | 2662 | 2662 | 2586 | 2544 | 2870 | 3495 | 2915 | 2860 | 3183 | 3721 | 4324 | 3887 | 4683 | 5071 | 3456 | 3081 | 3058 | 3197 | 934 | 1395 | 1814 | 1906 | 1333 | 1278 | 1166 | 2017 | 2758 | 3242 | 4805 | 4721 | 4826 | 5076 | 8522 | 12036 | 16903 | 14965 | 13587 | 10900 |
# attacks_per_year.loc[attacks_per_year['number of attacks per year'].idxmax(), 'Year']
# sns.lineplot(x='Year', y='number of attacks per year', data=attacks_per_year, color='b',marker="o")
plt.style.use('ggplot')
plt.figure(figsize=(12, 8))
# Create the line plot
sns.lineplot(
x='Year',y='number of attacks per year',data=attacks_per_year,color='b',marker='o',
# linestyle=':', # Line style
linewidth=2, markersize=8,
markerfacecolor='red', # Marker fill color
markeredgecolor='black' # Marker edge color
)
# Add gridlines for better readability
plt.grid(True, linestyle='--', alpha=0.6)
# Add labels and title
plt.xlabel('Year', fontsize=14, fontweight='bold')
plt.ylabel('Number of Attacks', fontsize=14, fontweight='bold')
plt.title("Terrorism Attacks Trends from 1970 to 2017", fontsize=16, fontweight='bold')
# Add annotations or highlights (optional)
plt.annotate('Peak', xy=(attacks_per_year.loc[attacks_per_year['number of attacks per year'].idxmax(), 'Year'],attacks_per_year['number of attacks per year'].max()),
xytext=(2012, attacks_per_year['number of attacks per year'].max()+1500),
arrowprops=dict(facecolor='black'))
# Display the plot
plt.show()
2014.¶attacks_per_year.nlargest(5,'number of attacks per year')
| Year | number of attacks per year | |
|---|---|---|
| 43 | 2014 | 16903 |
| 44 | 2015 | 14965 |
| 45 | 2016 | 13587 |
| 42 | 2013 | 12036 |
| 46 | 2017 | 10900 |
# Number Of Terrorist Activities Each Year
plt.style.use('fivethirtyeight')
plt.subplots(figsize=(15,6))
sns.countplot(x='Year',data=df_terr,palette='rocket',edgecolor=sns.color_palette('magma',5))
plt.xlabel('Attack Year', fontsize=14)
plt.ylabel('Number of Attacks each year', fontsize=14)
plt.xticks(rotation=45,fontsize=10)
plt.yticks(fontsize=10)
plt.title('Number Of Terrorist Activities Each Year',fontsize=15, fontweight='bold')
# plt.tight_layout()
plt.show()
# numbers terrrosim attacks on region
attacks_per_region = df_terr.groupby('Region').size().reset_index(name='Total_attacks_region')
attacks_per_region.sort_values(by='Total_attacks_region',ascending=False)
| Region | Total_attacks_region | |
|---|---|---|
| 5 | Middle East & North Africa | 50474 |
| 8 | South Asia | 44974 |
| 7 | South America | 18978 |
| 10 | Sub-Saharan Africa | 17550 |
| 11 | Western Europe | 16639 |
| 9 | Southeast Asia | 12485 |
| 1 | Central America & Caribbean | 10344 |
| 4 | Eastern Europe | 5144 |
| 6 | North America | 3456 |
| 3 | East Asia | 802 |
| 2 | Central Asia | 563 |
| 0 | Australasia & Oceania | 282 |
plt.style.use('ggplot')
plt.subplots(figsize=(15, 6))
sns.barplot(x='Region',y='Total_attacks_region' ,
data=attacks_per_region,palette='hot',
order=df_terr['Region'].value_counts().index,edgecolor=sns.color_palette('rocket',5))
# Customize y-axis
plt.yticks(fontsize=12)
# Add labels and title
plt.xlabel('Region', fontsize=14,fontweight='bold')
plt.ylabel('Total Attacks', fontsize=14,fontweight='bold')
plt.title('Number Of Terrorist Activities by Region', fontsize=15, fontweight='bold')
plt.xticks(rotation=45,fontsize=12,ha='right')
plt.yticks(fontsize=12)
# Adjust layout to prevent overlapping
# plt.tight_layout()
# Show the plot
plt.show()
Middle East and North Africa are the most terrorism prone regions followed by South Asia. The Australian Region have experienced very few terrorist events.
Collectively we can say that The African and Asian Continent experience the highest terrorist attacks.region each year.¶# sheet showing number terrosim attacks on region per year .
attc_region_per_year=pd.crosstab(df_terr.Year,df_terr.Region)
attc_region_per_year['[sum_attacks on region per year]'] = attc_region_per_year.sum(axis=1)
attc_region_per_year.loc['[Total_attac_region]']= attc_region_per_year.sum()
attc_region_per_year.T
| Year | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | 1978 | 1979 | 1980 | 1981 | 1982 | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | [Total_attac_region] |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | ||||||||||||||||||||||||||||||||||||||||||||||||
| Australasia & Oceania | 1 | 1 | 8 | 1 | 1 | 0 | 0 | 0 | 2 | 2 | 7 | 3 | 2 | 0 | 11 | 7 | 4 | 3 | 12 | 29 | 18 | 10 | 17 | 14 | 18 | 19 | 7 | 6 | 4 | 6 | 4 | 2 | 4 | 0 | 0 | 2 | 1 | 8 | 1 | 1 | 0 | 0 | 1 | 9 | 14 | 10 | 12 | 282 |
| Central America & Caribbean | 7 | 5 | 3 | 6 | 11 | 9 | 45 | 24 | 199 | 609 | 1070 | 1148 | 996 | 858 | 681 | 780 | 393 | 566 | 495 | 503 | 316 | 729 | 212 | 180 | 168 | 116 | 117 | 1 | 8 | 14 | 8 | 3 | 8 | 5 | 3 | 5 | 4 | 0 | 9 | 1 | 1 | 1 | 14 | 5 | 1 | 3 | 4 | 10344 |
| Central Asia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 77 | 65 | 55 | 33 | 49 | 25 | 24 | 21 | 18 | 6 | 7 | 8 | 11 | 6 | 4 | 36 | 31 | 9 | 9 | 12 | 7 | 9 | 10 | 17 | 7 | 563 |
| East Asia | 2 | 1 | 0 | 2 | 4 | 12 | 2 | 4 | 35 | 16 | 1 | 4 | 3 | 13 | 15 | 10 | 12 | 10 | 24 | 18 | 99 | 29 | 74 | 34 | 38 | 89 | 40 | 9 | 4 | 19 | 19 | 4 | 6 | 4 | 2 | 1 | 0 | 25 | 8 | 1 | 4 | 4 | 15 | 43 | 28 | 8 | 7 | 802 |
| Eastern Europe | 12 | 5 | 1 | 1 | 2 | 0 | 0 | 2 | 2 | 1 | 1 | 4 | 3 | 2 | 4 | 5 | 3 | 1 | 4 | 16 | 58 | 84 | 91 | 95 | 68 | 175 | 248 | 105 | 138 | 234 | 252 | 112 | 100 | 46 | 76 | 70 | 62 | 209 | 165 | 261 | 198 | 173 | 165 | 962 | 684 | 134 | 110 | 5144 |
| Middle East & North Africa | 28 | 55 | 53 | 19 | 42 | 44 | 55 | 211 | 128 | 455 | 437 | 312 | 290 | 334 | 268 | 133 | 196 | 202 | 246 | 465 | 494 | 612 | 1192 | 1051 | 590 | 373 | 548 | 247 | 316 | 272 | 362 | 326 | 310 | 492 | 882 | 1187 | 1385 | 1536 | 1361 | 1463 | 1663 | 2409 | 4560 | 6939 | 6036 | 6115 | 3780 | 50474 |
| North America | 472 | 247 | 73 | 64 | 111 | 159 | 125 | 149 | 117 | 79 | 75 | 77 | 86 | 47 | 67 | 44 | 53 | 35 | 30 | 44 | 37 | 40 | 41 | 101 | 94 | 111 | 137 | 37 | 56 | 41 | 48 | 33 | 34 | 12 | 22 | 15 | 18 | 31 | 16 | 24 | 12 | 39 | 32 | 37 | 62 | 75 | 97 | 3456 |
| South America | 65 | 24 | 33 | 83 | 81 | 55 | 91 | 119 | 222 | 236 | 319 | 383 | 639 | 950 | 1492 | 1040 | 1184 | 1265 | 1041 | 1385 | 1076 | 1322 | 1204 | 373 | 255 | 517 | 774 | 108 | 134 | 150 | 229 | 162 | 117 | 42 | 49 | 50 | 47 | 144 | 159 | 148 | 106 | 133 | 182 | 283 | 176 | 159 | 172 | 18978 |
| South Asia | 1 | 0 | 1 | 1 | 2 | 4 | 4 | 2 | 2 | 34 | 12 | 23 | 20 | 63 | 244 | 161 | 273 | 348 | 801 | 926 | 601 | 677 | 545 | 376 | 1051 | 739 | 490 | 136 | 225 | 357 | 385 | 334 | 353 | 369 | 604 | 938 | 982 | 1759 | 1945 | 1981 | 2138 | 3803 | 4612 | 4998 | 4585 | 3639 | 3430 | 44974 |
| Southeast Asia | 10 | 6 | 16 | 2 | 3 | 7 | 12 | 8 | 44 | 86 | 87 | 50 | 43 | 22 | 46 | 128 | 102 | 163 | 242 | 203 | 348 | 206 | 279 | 151 | 168 | 193 | 159 | 32 | 107 | 256 | 185 | 110 | 145 | 95 | 204 | 272 | 365 | 514 | 561 | 473 | 356 | 587 | 1188 | 1082 | 1072 | 1077 | 1020 | 12485 |
| Sub-Saharan Africa | 3 | 2 | 4 | 4 | 7 | 12 | 11 | 29 | 46 | 124 | 58 | 98 | 60 | 106 | 126 | 141 | 185 | 174 | 337 | 291 | 450 | 271 | 570 | 432 | 239 | 217 | 279 | 87 | 143 | 191 | 162 | 121 | 73 | 34 | 60 | 114 | 302 | 380 | 283 | 331 | 494 | 1168 | 999 | 2321 | 1964 | 2077 | 1970 | 17550 |
| Western Europe | 50 | 125 | 376 | 290 | 317 | 438 | 578 | 771 | 729 | 1020 | 595 | 484 | 402 | 475 | 541 | 466 | 455 | 416 | 489 | 444 | 390 | 696 | 769 | 584 | 337 | 476 | 349 | 141 | 236 | 253 | 234 | 120 | 121 | 59 | 104 | 98 | 72 | 163 | 182 | 133 | 95 | 193 | 261 | 215 | 333 | 273 | 291 | 16639 |
| [sum_attacks on region per year] | 651 | 471 | 568 | 473 | 581 | 740 | 923 | 1319 | 1526 | 2662 | 2662 | 2586 | 2544 | 2870 | 3495 | 2915 | 2860 | 3183 | 3721 | 4324 | 3887 | 4683 | 5071 | 3456 | 3081 | 3058 | 3197 | 934 | 1395 | 1814 | 1906 | 1333 | 1278 | 1166 | 2017 | 2758 | 3242 | 4805 | 4721 | 4826 | 5076 | 8522 | 12036 | 16903 | 14965 | 13587 | 10900 | 181691 |
country¶# total terssiom attacks on country each year.
attacks_country = df_terr.Country.value_counts().to_frame().reset_index()
attacks_country.columns=['Country','total_attacks per country']
attacks_country.T
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | Iraq | Pakistan | Afghanistan | India | Colombia | Philippines | Peru | El Salvador | United Kingdom | Turkey | Somalia | Nigeria | Thailand | Yemen | Spain | Sri Lanka | United States | Algeria | France | Egypt | Lebanon | Chile | Libya | West Bank and Gaza Strip | Syria | Russia | Israel | Guatemala | South Africa | Nicaragua | Ukraine | Bangladesh | Italy | Greece | Nepal | Sudan | Argentina | Democratic Republic of the Congo | Indonesia | Germany | Iran | Kenya | Burundi | Mali | Myanmar | West Germany (FRG) | Mexico | Angola | Japan | Uganda | Saudi Arabia | Mozambique | Cameroon | Honduras | Bolivia | Ireland | Venezuela | Central African Republic | Brazil | Cambodia | China | South Sudan | Ecuador | Georgia | Haiti | Bahrain | Yugoslavia | Kosovo | Ethiopia | Tajikistan | Bosnia-Herzegovina | Rwanda | Niger | Belgium | Namibia | Portugal | Sweden | Cyprus | Netherlands | Panama | Senegal | Macedonia | Austria | Australia | Paraguay | Jordan | Switzerland | Tunisia | Zimbabwe | Sierra Leone | Malaysia | Canada | Chad | Dominican Republic | Papua New Guinea | Rhodesia | Uruguay | Albania | Soviet Union | Kuwait | Ivory Coast | Costa Rica | Suriname | Zambia | Tanzania | Croatia | Guadeloupe | Bulgaria | Burkina Faso | Zaire | Taiwan | Azerbaijan | Togo | Hungary | Denmark | Poland | South Korea | East Germany (GDR) | Morocco | Jamaica | Republic of the Congo | Kyrgyzstan | Liberia | Macau | Czech Republic | New Caledonia | Cuba | Lesotho | Kazakhstan | Madagascar | Laos | Guyana | Hong Kong | Guinea | Armenia | Malta | Maldives | United Arab Emirates | Trinidad and Tobago | Djibouti | Uzbekistan | Moldova | Finland | New Zealand | Ghana | Norway | Mauritania | Slovak Republic | Fiji | Latvia | Swaziland | Estonia | Luxembourg | Belarus | Vietnam | Martinique | Serbia | Serbia-Montenegro | East Timor | Botswana | Eritrea | Czechoslovakia | Guinea-Bissau | Gabon | Lithuania | Benin | Belize | Qatar | Singapore | French Guiana | Romania | Bhutan | North Yemen | Slovenia | Brunei | Grenada | Bahamas | Montenegro | Western Sahara | Comoros | Malawi | People's Republic of the Congo | Iceland | Solomon Islands | Gambia | Dominica | French Polynesia | Barbados | Vanuatu | Turkmenistan | Seychelles | Mauritius | St. Kitts and Nevis | Equatorial Guinea | South Yemen | Vatican City | Falkland Islands | St. Lucia | North Korea | New Hebrides | International | Wallis and Futuna | South Vietnam | Andorra | Antigua and Barbuda |
| total_attacks per country | 24636 | 14368 | 12731 | 11960 | 8306 | 6908 | 6096 | 5320 | 5235 | 4292 | 4142 | 3907 | 3849 | 3347 | 3249 | 3022 | 2836 | 2743 | 2693 | 2479 | 2478 | 2365 | 2249 | 2227 | 2201 | 2194 | 2183 | 2050 | 2016 | 1970 | 1709 | 1648 | 1565 | 1275 | 1215 | 967 | 815 | 775 | 761 | 735 | 684 | 683 | 613 | 566 | 546 | 541 | 524 | 499 | 402 | 394 | 371 | 363 | 332 | 323 | 314 | 307 | 293 | 283 | 273 | 259 | 252 | 225 | 220 | 217 | 213 | 207 | 203 | 196 | 190 | 188 | 159 | 159 | 154 | 154 | 151 | 140 | 132 | 132 | 130 | 127 | 118 | 118 | 115 | 114 | 114 | 113 | 111 | 109 | 101 | 101 | 99 | 96 | 91 | 90 | 89 | 83 | 82 | 80 | 78 | 76 | 74 | 67 | 66 | 62 | 59 | 57 | 56 | 52 | 52 | 50 | 50 | 49 | 48 | 46 | 41 | 39 | 38 | 38 | 36 | 36 | 36 | 35 | 34 | 33 | 32 | 31 | 30 | 29 | 27 | 27 | 27 | 26 | 26 | 25 | 24 | 23 | 22 | 22 | 22 | 22 | 21 | 21 | 20 | 20 | 19 | 19 | 18 | 18 | 17 | 17 | 16 | 16 | 16 | 13 | 12 | 12 | 12 | 11 | 10 | 10 | 10 | 10 | 9 | 8 | 8 | 8 | 8 | 7 | 7 | 7 | 6 | 6 | 6 | 6 | 6 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
# df_terr.groupby('Country').size()
# attacks_country = df_terr.groupby('Country').size().reset_index()
# attacks_country
# plt.style.use('fivethirtyeight')
plt.style.use('ggplot')
plt.figure(figsize=(15, 6))
# Assuming `df_terr` is your DataFrame with a 'Country' column
top_countries = df_terr['Country'].value_counts().nlargest(15)
sns.barplot(y=top_countries.index, x=top_countries.values, palette='YlOrRd_r',orient="h",edgecolor=sns.color_palette('gist_heat',4))
plt.title('Top 15 Countries Affected by Terrorist Activities', fontsize=15, fontweight='bold')
plt.ylabel('Countries', fontsize=17,fontweight='semibold')
plt.xlabel('Count', fontsize=14,fontweight='semibold')
plt.xticks(rotation=45, fontsize=13)
plt.yticks(fontsize=13)
plt.tight_layout()
plt.show()
From the graph we can see The Most 5 Targered Affected Country with Terrorism Attacks are:
iraq has witnessed a very large number of terrorist activities followed by Pakistan,Afghanistan..etc .
One thing to note is the countries with highest attacks, are mostly densely populated countries, thus it will eventually claim many lives.country each year.¶attac_country_per_year = pd.crosstab(df_terr.Country,df_terr.Year)
attac_country_per_year["[sum_per_year]"] = attac_country_per_year.sum(axis=1)
attac_country_per_year.loc["[sum_attacks on country per year]"] =attac_country_per_year.sum()
attac_country_per_year.T
| Country | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia-Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burundi | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Comoros | Costa Rica | Croatia | Cuba | Cyprus | Czech Republic | Czechoslovakia | Democratic Republic of the Congo | Denmark | Djibouti | Dominica | Dominican Republic | East Germany (GDR) | East Timor | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Ethiopia | Falkland Islands | Fiji | Finland | France | French Guiana | French Polynesia | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guadeloupe | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Honduras | Hong Kong | Hungary | Iceland | India | Indonesia | International | Iran | Iraq | Ireland | Israel | Italy | Ivory Coast | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Lesotho | Liberia | Libya | Lithuania | Luxembourg | Macau | Macedonia | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Martinique | Mauritania | Mauritius | Mexico | Moldova | Montenegro | Morocco | Mozambique | Myanmar | Namibia | Nepal | Netherlands | New Caledonia | New Hebrides | New Zealand | Nicaragua | Niger | Nigeria | North Korea | North Yemen | Norway | Pakistan | Panama | Papua New Guinea | Paraguay | People's Republic of the Congo | Peru | Philippines | Poland | Portugal | Qatar | Republic of the Congo | Rhodesia | Romania | Russia | Rwanda | Saudi Arabia | Senegal | Serbia | Serbia-Montenegro | Seychelles | Sierra Leone | Singapore | Slovak Republic | Slovenia | Solomon Islands | Somalia | South Africa | South Korea | South Sudan | South Vietnam | South Yemen | Soviet Union | Spain | Sri Lanka | St. Kitts and Nevis | St. Lucia | Sudan | Suriname | Swaziland | Sweden | Switzerland | Syria | Taiwan | Tajikistan | Tanzania | Thailand | Togo | Trinidad and Tobago | Tunisia | Turkey | Turkmenistan | Uganda | Ukraine | United Arab Emirates | United Kingdom | United States | Uruguay | Uzbekistan | Vanuatu | Vatican City | Venezuela | Vietnam | Wallis and Futuna | West Bank and Gaza Strip | West Germany (FRG) | Western Sahara | Yemen | Yugoslavia | Zaire | Zambia | Zimbabwe | [sum_attacks on country per year] |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 1970 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 12 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 1 | 0 | 3 | 0 | 0 | 2 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 12 | 468 | 33 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 651 |
| 1971 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 0 | 0 | 0 | 0 | 81 | 247 | 8 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 1 | 0 | 471 |
| 1972 | 0 | 0 | 1 | 0 | 0 | 0 | 20 | 0 | 8 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 0 | 0 | 0 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 15 | 0 | 5 | 15 | 4 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 292 | 68 | 7 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 24 | 0 | 0 | 0 | 3 | 0 | 0 | 568 |
| 1973 | 1 | 0 | 0 | 0 | 0 | 0 | 60 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 6 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 3 | 1 | 15 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 189 | 58 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 27 | 0 | 0 | 1 | 1 | 1 | 0 | 473 |
| 1974 | 0 | 0 | 0 | 1 | 0 | 0 | 71 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 3 | 12 | 22 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 203 | 94 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 28 | 0 | 0 | 1 | 0 | 0 | 0 | 581 |
| 1975 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 13 | 1 | 7 | 2 | 7 | 0 | 0 | 12 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 136 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 194 | 149 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 35 | 0 | 0 | 0 | 0 | 1 | 0 | 740 |
| 1976 | 0 | 0 | 1 | 0 | 0 | 0 | 54 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 22 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 13 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 3 | 5 | 2 | 173 | 0 | 1 | 1 | 1 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 2 | 0 | 1 | 0 | 0 | 10 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 35 | 0 | 0 | 0 | 0 | 194 | 105 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 923 |
| 1977 | 0 | 0 | 0 | 0 | 1 | 0 | 17 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 80 | 0 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 7 | 1 | 6 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 53 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 47 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 5 | 0 | 3 | 6 | 308 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 4 | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 147 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 189 | 0 | 0 | 0 | 1 | 140 | 130 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 40 | 0 | 0 | 1 | 1 | 1 | 0 | 1319 |
| 1978 | 0 | 0 | 1 | 0 | 2 | 0 | 25 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 3 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 8 | 0 | 158 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 11 | 2 | 91 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 0 | 0 | 29 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 27 | 0 | 3 | 23 | 277 | 0 | 0 | 35 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 0 | 0 | 1 | 2 | 0 | 8 | 0 | 3 | 0 | 0 | 0 | 76 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 4 | 36 | 0 | 4 | 0 | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 1 | 209 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 1 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 1 | 100 | 87 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 4 | 19 | 1 | 0 | 0 | 0 | 3 | 0 | 1526 |
| 1979 | 3 | 0 | 1 | 0 | 3 | 0 | 16 | 0 | 2 | 5 | 0 | 0 | 1 | 1 | 0 | 0 | 8 | 0 | 0 | 0 | 14 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 0 | 140 | 0 | 2 | 0 | 0 | 5 | 0 | 0 | 0 | 3 | 1 | 0 | 2 | 0 | 0 | 2 | 1 | 326 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 212 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 60 | 0 | 0 | 2 | 0 | 6 | 0 | 0 | 0 | 20 | 0 | 0 | 82 | 2 | 8 | 130 | 209 | 0 | 2 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 2 | 5 | 19 | 31 | 0 | 3 | 0 | 0 | 0 | 206 | 0 | 0 | 0 | 0 | 1 | 7 | 4 | 0 | 0 | 0 | 3 | 50 | 1 | 15 | 0 | 0 | 51 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 279 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 28 | 0 | 0 | 1 | 16 | 0 | 1 | 1 | 141 | 0 | 8 | 0 | 2 | 239 | 69 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 22 | 17 | 0 | 0 | 0 | 0 | 1 | 0 | 2662 |
| 1980 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 6 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | 8 | 1 | 0 | 0 | 35 | 0 | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 41 | 0 | 141 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 5 | 710 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 94 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 4 | 9 | 281 | 2 | 0 | 1 | 0 | 16 | 1 | 0 | 0 | 10 | 0 | 0 | 67 | 6 | 7 | 79 | 110 | 0 | 6 | 0 | 1 | 0 | 1 | 0 | 4 | 0 | 0 | 0 | 86 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 1 | 0 | 35 | 0 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 2 | 0 | 64 | 60 | 0 | 8 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 23 | 0 | 0 | 0 | 0 | 0 | 187 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 33 | 0 | 0 | 0 | 26 | 0 | 0 | 1 | 95 | 0 | 0 | 0 | 1 | 134 | 67 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 56 | 20 | 0 | 0 | 1 | 0 | 0 | 14 | 2662 |
| 1981 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 3 | 0 | 1 | 0 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 5 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 34 | 0 | 172 | 0 | 10 | 0 | 0 | 3 | 0 | 0 | 0 | 6 | 0 | 2 | 0 | 0 | 0 | 2 | 9 | 664 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 1 | 399 | 0 | 0 | 1 | 0 | 17 | 3 | 0 | 0 | 16 | 1 | 0 | 108 | 3 | 5 | 36 | 69 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 81 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 47 | 0 | 0 | 0 | 1 | 1 | 4 | 4 | 0 | 0 | 0 | 149 | 31 | 1 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 8 | 42 | 0 | 0 | 0 | 0 | 0 | 107 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 13 | 41 | 0 | 0 | 0 | 18 | 0 | 1 | 0 | 8 | 0 | 25 | 0 | 3 | 142 | 74 | 0 | 0 | 0 | 1 | 7 | 0 | 0 | 17 | 31 | 0 | 0 | 2 | 0 | 1 | 9 | 2586 |
| 1982 | 0 | 0 | 0 | 0 | 2 | 0 | 9 | 0 | 2 | 11 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 27 | 0 | 0 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 21 | 0 | 222 | 0 | 6 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 1 | 537 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 62 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 0 | 0 | 354 | 0 | 0 | 0 | 0 | 34 | 0 | 0 | 0 | 13 | 0 | 0 | 26 | 5 | 4 | 71 | 23 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 120 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 9 | 2 | 2 | 0 | 2 | 0 | 0 | 0 | 63 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 350 | 38 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 1 | 0 | 0 | 0 | 0 | 146 | 2 | 0 | 0 | 3 | 1 | 1 | 0 | 6 | 20 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 5 | 0 | 1 | 0 | 0 | 95 | 78 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 39 | 29 | 0 | 0 | 0 | 0 | 0 | 12 | 2544 |
| 1983 | 0 | 0 | 0 | 0 | 7 | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 27 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 106 | 0 | 234 | 0 | 3 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 10 | 0 | 371 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 121 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 1 | 17 | 117 | 0 | 0 | 0 | 6 | 29 | 0 | 0 | 1 | 47 | 0 | 0 | 10 | 3 | 4 | 31 | 7 | 0 | 0 | 12 | 7 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 234 | 12 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | 0 | 0 | 2 | 0 | 0 | 0 | 4 | 4 | 5 | 0 | 2 | 0 | 0 | 0 | 299 | 0 | 3 | 0 | 0 | 0 | 9 | 9 | 0 | 0 | 0 | 536 | 16 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 56 | 1 | 0 | 0 | 1 | 0 | 122 | 7 | 0 | 0 | 3 | 1 | 0 | 1 | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 5 | 0 | 6 | 0 | 1 | 177 | 44 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 34 | 5 | 0 | 0 | 1 | 0 | 0 | 6 | 2870 |
| 1984 | 0 | 0 | 0 | 0 | 11 | 0 | 46 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 10 | 0 | 0 | 0 | 14 | 0 | 0 | 5 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 563 | 0 | 237 | 0 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 24 | 0 | 273 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 145 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 | 14 | 74 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 159 | 2 | 0 | 17 | 2 | 1 | 25 | 14 | 0 | 0 | 15 | 5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 166 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 2 | 0 | 0 | 4 | 0 | 0 | 0 | 10 | 0 | 24 | 0 | 1 | 10 | 0 | 1 | 302 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 2 | 0 | 592 | 43 | 0 | 14 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 60 | 0 | 0 | 0 | 0 | 0 | 143 | 81 | 0 | 0 | 5 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 19 | 0 | 3 | 0 | 0 | 145 | 63 | 4 | 0 | 0 | 0 | 4 | 0 | 0 | 26 | 21 | 1 | 0 | 1 | 3 | 0 | 3 | 3495 |
| 1985 | 0 | 0 | 0 | 0 | 6 | 0 | 43 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 21 | 0 | 0 | 0 | 13 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 232 | 0 | 382 | 0 | 5 | 0 | 0 | 7 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 4 | 0 | 12 | 2 | 436 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 106 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 0 | 9 | 63 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 39 | 2 | 0 | 3 | 0 | 2 | 21 | 11 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 95 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 5 | 0 | 8 | 9 | 4 | 7 | 0 | 0 | 258 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 352 | 124 | 0 | 20 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 106 | 0 | 0 | 0 | 0 | 0 | 114 | 110 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 2 | 0 | 7 | 0 | 2 | 67 | 40 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 53 | 0 | 0 | 0 | 0 | 1 | 1 | 2915 |
| 1986 | 0 | 0 | 0 | 0 | 5 | 0 | 33 | 0 | 2 | 4 | 0 | 0 | 0 | 11 | 0 | 0 | 3 | 0 | 0 | 0 | 4 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 243 | 0 | 307 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 8 | 4 | 175 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 95 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 0 | 1 | 31 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 1 | 96 | 4 | 0 | 6 | 0 | 2 | 48 | 7 | 0 | 1 | 10 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 106 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 7 | 0 | 1 | 0 | 14 | 2 | 0 | 0 | 171 | 0 | 0 | 0 | 0 | 0 | 24 | 5 | 0 | 0 | 0 | 568 | 80 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 159 | 2 | 0 | 0 | 0 | 0 | 125 | 142 | 0 | 0 | 6 | 8 | 1 | 3 | 0 | 4 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 7 | 0 | 1 | 0 | 1 | 95 | 49 | 2 | 0 | 0 | 0 | 8 | 0 | 0 | 13 | 46 | 1 | 0 | 0 | 0 | 1 | 1 | 2860 |
| 1987 | 1 | 0 | 0 | 0 | 3 | 0 | 80 | 0 | 0 | 4 | 0 | 0 | 0 | 6 | 0 | 0 | 1 | 0 | 0 | 0 | 14 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 173 | 0 | 337 | 0 | 3 | 0 | 0 | 10 | 0 | 0 | 0 | 2 | 1 | 0 | 15 | 1 | 0 | 6 | 7 | 234 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 87 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 26 | 0 | 0 | 56 | 0 | 0 | 0 | 10 | 20 | 0 | 0 | 0 | 166 | 0 | 0 | 1 | 3 | 24 | 23 | 14 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 94 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 0 | 4 | 11 | 1 | 7 | 0 | 1 | 0 | 0 | 0 | 223 | 0 | 0 | 0 | 0 | 2 | 60 | 4 | 0 | 1 | 0 | 627 | 160 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 133 | 0 | 0 | 0 | 0 | 0 | 104 | 115 | 0 | 0 | 1 | 13 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 43 | 0 | 1 | 0 | 0 | 117 | 34 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 11 | 19 | 0 | 0 | 0 | 0 | 1 | 11 | 3183 |
| 1988 | 11 | 0 | 0 | 0 | 12 | 0 | 33 | 0 | 3 | 1 | 0 | 0 | 0 | 36 | 0 | 0 | 5 | 0 | 0 | 0 | 11 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 185 | 0 | 427 | 0 | 2 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 4 | 0 | 9 | 9 | 367 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 4 | 29 | 0 | 0 | 0 | 6 | 21 | 0 | 0 | 0 | 358 | 0 | 0 | 6 | 4 | 0 | 35 | 29 | 0 | 0 | 16 | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 125 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 16 | 23 | 17 | 0 | 4 | 9 | 0 | 0 | 62 | 0 | 2 | 0 | 1 | 0 | 44 | 1 | 0 | 0 | 0 | 355 | 210 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 245 | 8 | 0 | 0 | 0 | 0 | 151 | 350 | 0 | 0 | 10 | 9 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 42 | 0 | 15 | 0 | 0 | 181 | 27 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 18 | 14 | 0 | 0 | 0 | 1 | 5 | 1 | 3721 |
| 1989 | 10 | 0 | 0 | 0 | 12 | 0 | 32 | 0 | 2 | 2 | 0 | 0 | 0 | 36 | 0 | 0 | 7 | 0 | 0 | 0 | 40 | 0 | 2 | 5 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 146 | 3 | 492 | 0 | 1 | 0 | 0 | 6 | 0 | 4 | 0 | 1 | 0 | 0 | 11 | 1 | 0 | 14 | 7 | 356 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 79 | 0 | 0 | 0 | 3 | 29 | 1 | 1 | 0 | 324 | 2 | 0 | 6 | 4 | 3 | 87 | 15 | 0 | 0 | 13 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 141 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 64 | 24 | 11 | 0 | 5 | 1 | 0 | 0 | 21 | 1 | 0 | 0 | 0 | 0 | 45 | 2 | 24 | 0 | 0 | 630 | 156 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 149 | 1 | 0 | 0 | 0 | 7 | 142 | 510 | 0 | 0 | 8 | 17 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 16 | 0 | 1 | 0 | 114 | 0 | 15 | 0 | 1 | 163 | 42 | 1 | 0 | 0 | 0 | 8 | 0 | 0 | 100 | 21 | 0 | 0 | 3 | 0 | 18 | 3 | 4324 |
| 1990 | 2 | 1 | 2 | 0 | 205 | 0 | 31 | 0 | 0 | 1 | 0 | 0 | 0 | 22 | 0 | 0 | 2 | 0 | 0 | 0 | 11 | 0 | 1 | 7 | 0 | 5 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 161 | 1 | 349 | 0 | 1 | 0 | 0 | 11 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 3 | 0 | 8 | 13 | 184 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 30 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 55 | 0 | 0 | 83 | 0 | 0 | 5 | 3 | 13 | 1 | 0 | 0 | 349 | 3 | 0 | 11 | 0 | 2 | 98 | 6 | 0 | 0 | 93 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 84 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 23 | 9 | 2 | 0 | 6 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 87 | 15 | 18 | 0 | 0 | 495 | 320 | 7 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 154 | 3 | 0 | 0 | 0 | 36 | 113 | 141 | 0 | 0 | 1 | 7 | 0 | 6 | 1 | 0 | 1 | 0 | 0 | 10 | 0 | 0 | 0 | 195 | 0 | 14 | 0 | 0 | 147 | 32 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 88 | 9 | 0 | 0 | 5 | 0 | 4 | 6 | 3887 |
| 1991 | 30 | 1 | 30 | 0 | 16 | 0 | 27 | 1 | 4 | 3 | 3 | 0 | 1 | 42 | 0 | 0 | 6 | 3 | 0 | 0 | 28 | 0 | 0 | 24 | 0 | 3 | 2 | 7 | 3 | 4 | 0 | 4 | 2 | 127 | 1 | 420 | 0 | 2 | 26 | 1 | 11 | 0 | 1 | 0 | 1 | 1 | 0 | 8 | 0 | 0 | 17 | 10 | 500 | 0 | 0 | 0 | 5 | 0 | 3 | 0 | 137 | 0 | 0 | 0 | 0 | 3 | 65 | 0 | 57 | 0 | 1 | 77 | 0 | 0 | 0 | 17 | 21 | 1 | 3 | 0 | 339 | 7 | 0 | 9 | 3 | 16 | 64 | 31 | 0 | 1 | 19 | 11 | 0 | 5 | 0 | 5 | 0 | 0 | 0 | 91 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 12 | 0 | 1 | 0 | 0 | 10 | 1 | 0 | 3 | 35 | 22 | 1 | 1 | 8 | 0 | 0 | 1 | 80 | 6 | 3 | 0 | 0 | 2 | 150 | 16 | 2 | 0 | 0 | 658 | 162 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 2 | 4 | 0 | 0 | 0 | 9 | 1 | 0 | 0 | 0 | 8 | 127 | 6 | 0 | 0 | 0 | 34 | 89 | 115 | 0 | 0 | 3 | 1 | 1 | 6 | 2 | 0 | 2 | 0 | 0 | 8 | 2 | 1 | 2 | 293 | 0 | 2 | 2 | 0 | 262 | 30 | 3 | 0 | 0 | 0 | 17 | 0 | 0 | 83 | 0 | 0 | 5 | 11 | 6 | 0 | 1 | 4683 |
| 1992 | 36 | 3 | 215 | 0 | 50 | 1 | 41 | 2 | 4 | 8 | 12 | 0 | 0 | 76 | 1 | 0 | 9 | 2 | 1 | 0 | 38 | 22 | 0 | 24 | 1 | 2 | 0 | 2 | 67 | 6 | 3 | 1 | 4 | 116 | 5 | 523 | 1 | 2 | 4 | 4 | 6 | 0 | 3 | 0 | 4 | 6 | 0 | 11 | 0 | 0 | 23 | 79 | 29 | 1 | 0 | 2 | 13 | 0 | 1 | 1 | 126 | 0 | 0 | 0 | 0 | 39 | 156 | 8 | 35 | 0 | 0 | 48 | 2 | 0 | 4 | 27 | 12 | 6 | 1 | 0 | 237 | 4 | 0 | 16 | 35 | 7 | 78 | 37 | 3 | 6 | 40 | 2 | 5 | 30 | 0 | 15 | 0 | 4 | 2 | 91 | 0 | 10 | 1 | 1 | 0 | 0 | 0 | 7 | 3 | 2 | 0 | 3 | 1 | 1 | 0 | 0 | 6 | 11 | 0 | 1 | 19 | 12 | 0 | 8 | 12 | 2 | 0 | 0 | 30 | 16 | 11 | 0 | 0 | 2 | 85 | 37 | 10 | 4 | 0 | 383 | 162 | 5 | 1 | 0 | 0 | 0 | 1 | 21 | 18 | 0 | 15 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 21 | 271 | 3 | 0 | 0 | 0 | 0 | 72 | 103 | 0 | 0 | 7 | 5 | 1 | 12 | 6 | 0 | 20 | 17 | 0 | 25 | 27 | 1 | 0 | 514 | 0 | 3 | 1 | 1 | 274 | 32 | 11 | 2 | 0 | 0 | 32 | 1 | 0 | 119 | 0 | 1 | 24 | 12 | 5 | 0 | 1 | 5071 |
| 1994 | 9 | 2 | 227 | 0 | 9 | 0 | 14 | 4 | 9 | 5 | 11 | 0 | 1 | 68 | 0 | 1 | 5 | 0 | 3 | 0 | 8 | 9 | 0 | 19 | 0 | 2 | 0 | 16 | 39 | 6 | 4 | 1 | 1 | 15 | 13 | 201 | 1 | 1 | 4 | 9 | 6 | 1 | 0 | 0 | 0 | 2 | 0 | 7 | 0 | 0 | 6 | 143 | 21 | 0 | 0 | 6 | 4 | 0 | 0 | 1 | 97 | 0 | 0 | 1 | 1 | 18 | 79 | 4 | 42 | 0 | 0 | 84 | 2 | 0 | 0 | 27 | 6 | 4 | 2 | 0 | 107 | 5 | 0 | 43 | 18 | 10 | 55 | 18 | 2 | 2 | 9 | 6 | 1 | 5 | 0 | 2 | 0 | 1 | 2 | 67 | 3 | 3 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 42 | 1 | 0 | 2 | 3 | 8 | 1 | 3 | 3 | 0 | 0 | 1 | 19 | 8 | 8 | 1 | 0 | 2 | 154 | 3 | 3 | 2 | 0 | 91 | 72 | 4 | 1 | 0 | 8 | 0 | 0 | 47 | 33 | 2 | 2 | 0 | 0 | 0 | 22 | 1 | 1 | 1 | 0 | 57 | 174 | 2 | 0 | 0 | 0 | 0 | 55 | 35 | 0 | 0 | 5 | 1 | 0 | 1 | 0 | 0 | 5 | 31 | 1 | 24 | 13 | 1 | 1 | 300 | 0 | 13 | 10 | 0 | 256 | 55 | 4 | 0 | 0 | 0 | 12 | 1 | 1 | 165 | 0 | 0 | 19 | 1 | 6 | 0 | 1 | 3456 |
| 1995 | 6 | 0 | 185 | 0 | 10 | 0 | 16 | 1 | 5 | 12 | 2 | 0 | 8 | 74 | 0 | 0 | 1 | 0 | 1 | 0 | 2 | 6 | 1 | 23 | 0 | 2 | 0 | 83 | 56 | 2 | 5 | 0 | 3 | 17 | 8 | 123 | 0 | 0 | 1 | 0 | 3 | 3 | 0 | 0 | 6 | 2 | 0 | 0 | 0 | 0 | 6 | 102 | 9 | 0 | 2 | 2 | 5 | 0 | 0 | 1 | 71 | 0 | 3 | 0 | 1 | 17 | 147 | 1 | 8 | 0 | 0 | 82 | 0 | 0 | 0 | 34 | 12 | 1 | 0 | 0 | 179 | 24 | 0 | 8 | 17 | 3 | 13 | 1 | 7 | 1 | 20 | 3 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 38 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 29 | 0 | 0 | 1 | 3 | 12 | 0 | 0 | 7 | 0 | 0 | 0 | 18 | 2 | 1 | 0 | 0 | 0 | 666 | 6 | 10 | 12 | 0 | 44 | 63 | 4 | 3 | 0 | 0 | 0 | 1 | 37 | 17 | 3 | 8 | 0 | 0 | 0 | 14 | 0 | 3 | 0 | 0 | 13 | 32 | 4 | 0 | 0 | 0 | 0 | 44 | 126 | 2 | 0 | 4 | 0 | 1 | 1 | 5 | 0 | 4 | 34 | 1 | 12 | 1 | 4 | 1 | 133 | 0 | 7 | 2 | 2 | 22 | 60 | 1 | 1 | 0 | 0 | 11 | 1 | 0 | 67 | 0 | 0 | 8 | 3 | 6 | 1 | 2 | 3081 |
| 1996 | 4 | 6 | 129 | 0 | 4 | 0 | 19 | 1 | 5 | 4 | 2 | 0 | 16 | 161 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 33 | 0 | 18 | 0 | 12 | 0 | 35 | 33 | 0 | 1 | 1 | 1 | 8 | 62 | 409 | 0 | 1 | 3 | 1 | 10 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 5 | 51 | 7 | 0 | 1 | 1 | 7 | 0 | 0 | 0 | 270 | 2 | 0 | 1 | 0 | 3 | 52 | 2 | 20 | 0 | 0 | 26 | 1 | 0 | 0 | 27 | 19 | 3 | 13 | 0 | 213 | 65 | 0 | 3 | 12 | 1 | 19 | 8 | 2 | 1 | 8 | 2 | 0 | 3 | 0 | 0 | 5 | 1 | 0 | 39 | 0 | 1 | 4 | 3 | 0 | 10 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 75 | 2 | 0 | 2 | 0 | 13 | 0 | 5 | 6 | 0 | 0 | 2 | 26 | 1 | 11 | 0 | 0 | 1 | 180 | 2 | 10 | 0 | 0 | 42 | 61 | 6 | 0 | 1 | 1 | 0 | 0 | 66 | 15 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 5 | 3 | 0 | 16 | 47 | 1 | 0 | 0 | 0 | 0 | 63 | 176 | 0 | 0 | 10 | 0 | 0 | 1 | 4 | 2 | 5 | 22 | 0 | 19 | 1 | 3 | 0 | 54 | 0 | 29 | 3 | 0 | 36 | 35 | 1 | 0 | 2 | 0 | 12 | 1 | 0 | 30 | 0 | 0 | 8 | 16 | 13 | 4 | 0 | 3058 |
| 1997 | 1 | 41 | 344 | 0 | 7 | 0 | 11 | 0 | 4 | 1 | 1 | 2 | 11 | 17 | 0 | 2 | 2 | 0 | 0 | 0 | 4 | 42 | 0 | 39 | 0 | 3 | 0 | 79 | 18 | 0 | 2 | 0 | 1 | 6 | 18 | 598 | 3 | 1 | 8 | 14 | 0 | 4 | 0 | 1 | 0 | 1 | 1 | 12 | 0 | 0 | 4 | 22 | 2 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 130 | 2 | 0 | 1 | 0 | 6 | 12 | 1 | 21 | 0 | 0 | 26 | 0 | 0 | 1 | 16 | 22 | 0 | 17 | 0 | 193 | 27 | 0 | 0 | 21 | 4 | 13 | 8 | 1 | 8 | 5 | 3 | 2 | 17 | 0 | 2 | 0 | 0 | 2 | 46 | 0 | 0 | 0 | 1 | 0 | 16 | 2 | 1 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 95 | 1 | 0 | 0 | 0 | 35 | 0 | 9 | 1 | 0 | 0 | 1 | 7 | 3 | 20 | 0 | 0 | 0 | 206 | 6 | 2 | 6 | 0 | 58 | 57 | 3 | 0 | 0 | 2 | 0 | 0 | 77 | 33 | 0 | 26 | 0 | 0 | 0 | 3 | 0 | 4 | 1 | 0 | 19 | 17 | 0 | 0 | 0 | 0 | 0 | 86 | 64 | 0 | 0 | 3 | 2 | 1 | 4 | 1 | 1 | 1 | 40 | 1 | 20 | 0 | 0 | 0 | 44 | 0 | 24 | 2 | 0 | 78 | 40 | 2 | 0 | 0 | 0 | 41 | 2 | 0 | 22 | 0 | 0 | 19 | 38 | 5 | 0 | 0 | 3197 |
| 1998 | 1 | 7 | 151 | 0 | 20 | 0 | 0 | 2 | 6 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 9 | 0 | 2 | 0 | 2 | 0 | 13 | 7 | 1 | 4 | 0 | 1 | 1 | 2 | 94 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 12 | 6 | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 61 | 3 | 0 | 5 | 7 | 3 | 14 | 6 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 4 | 23 | 0 | 0 | 0 | 0 | 0 | 6 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 37 | 0 | 0 | 0 | 0 | 5 | 18 | 2 | 0 | 0 | 0 | 0 | 0 | 26 | 9 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 2 | 0 | 0 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 17 | 35 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 11 | 1 | 4 | 0 | 0 | 0 | 23 | 0 | 18 | 0 | 0 | 63 | 31 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 12 | 0 | 0 | 7 | 46 | 0 | 0 | 0 | 934 |
| 1999 | 9 | 3 | 106 | 0 | 34 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 5 | 0 | 1 | 0 | 1 | 0 | 13 | 8 | 0 | 2 | 0 | 1 | 3 | 2 | 116 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 46 | 0 | 0 | 0 | 0 | 7 | 13 | 0 | 35 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 112 | 60 | 0 | 8 | 12 | 3 | 8 | 7 | 0 | 0 | 2 | 2 | 0 | 0 | 39 | 0 | 3 | 0 | 1 | 42 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 4 | 1 | 8 | 1 | 0 | 0 | 0 | 1 | 2 | 18 | 0 | 0 | 0 | 39 | 1 | 0 | 1 | 0 | 5 | 31 | 0 | 0 | 0 | 3 | 0 | 0 | 54 | 0 | 0 | 1 | 0 | 0 | 0 | 9 | 0 | 1 | 0 | 1 | 4 | 17 | 0 | 0 | 0 | 0 | 0 | 47 | 46 | 0 | 0 | 5 | 0 | 1 | 2 | 2 | 0 | 0 | 4 | 0 | 4 | 0 | 0 | 0 | 109 | 0 | 19 | 6 | 0 | 76 | 53 | 0 | 8 | 0 | 0 | 5 | 0 | 0 | 11 | 0 | 0 | 17 | 23 | 0 | 7 | 0 | 1395 |
| 2000 | 14 | 2 | 138 | 0 | 22 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 23 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 0 | 2 | 0 | 2 | 0 | 17 | 4 | 0 | 5 | 1 | 0 | 2 | 4 | 137 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 0 | 28 | 0 | 0 | 0 | 1 | 7 | 8 | 0 | 28 | 0 | 0 | 1 | 12 | 0 | 0 | 9 | 0 | 2 | 0 | 0 | 180 | 101 | 0 | 15 | 10 | 0 | 15 | 8 | 2 | 0 | 13 | 2 | 1 | 4 | 60 | 0 | 4 | 8 | 3 | 12 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 3 | 0 | 21 | 23 | 1 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 0 | 49 | 1 | 0 | 2 | 0 | 3 | 132 | 0 | 0 | 1 | 0 | 0 | 0 | 138 | 1 | 5 | 4 | 0 | 0 | 0 | 24 | 0 | 1 | 1 | 1 | 9 | 21 | 0 | 0 | 0 | 0 | 0 | 112 | 68 | 0 | 1 | 7 | 0 | 0 | 1 | 4 | 0 | 0 | 6 | 2 | 4 | 0 | 0 | 1 | 35 | 0 | 20 | 1 | 0 | 61 | 32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 | 0 | 10 | 14 | 0 | 8 | 1 | 1814 |
| 2001 | 14 | 1 | 113 | 0 | 40 | 0 | 2 | 2 | 2 | 0 | 4 | 0 | 0 | 15 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 0 | 1 | 0 | 37 | 7 | 0 | 0 | 1 | 0 | 3 | 12 | 207 | 0 | 1 | 3 | 0 | 1 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 21 | 0 | 0 | 0 | 0 | 4 | 8 | 0 | 14 | 0 | 0 | 2 | 0 | 1 | 1 | 5 | 0 | 1 | 1 | 0 | 234 | 105 | 0 | 4 | 3 | 2 | 79 | 11 | 1 | 0 | 3 | 2 | 1 | 7 | 17 | 2 | 1 | 1 | 0 | 5 | 0 | 3 | 0 | 0 | 0 | 0 | 67 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 3 | 6 | 33 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 1 | 53 | 0 | 0 | 0 | 0 | 3 | 50 | 1 | 0 | 1 | 1 | 0 | 0 | 135 | 2 | 3 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 79 | 36 | 0 | 0 | 9 | 0 | 0 | 0 | 2 | 0 | 2 | 6 | 4 | 14 | 0 | 0 | 1 | 19 | 0 | 24 | 0 | 0 | 94 | 41 | 0 | 0 | 0 | 0 | 5 | 4 | 0 | 123 | 0 | 0 | 7 | 20 | 0 | 0 | 3 | 1906 |
| 2002 | 38 | 0 | 132 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 1 | 0 | 18 | 3 | 0 | 0 | 0 | 4 | 1 | 2 | 150 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 32 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 184 | 43 | 1 | 0 | 6 | 0 | 75 | 7 | 1 | 0 | 2 | 2 | 0 | 2 | 5 | 1 | 2 | 0 | 0 | 8 | 0 | 3 | 0 | 0 | 0 | 0 | 8 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 58 | 2 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 46 | 0 | 1 | 0 | 0 | 2 | 48 | 0 | 0 | 0 | 3 | 0 | 0 | 89 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 8 | 0 | 0 | 0 | 0 | 0 | 41 | 3 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 12 | 0 | 0 | 1 | 5 | 1 | 24 | 3 | 0 | 21 | 33 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 86 | 0 | 0 | 7 | 3 | 0 | 1 | 8 | 1333 |
| 2003 | 100 | 1 | 75 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 8 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 18 | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 98 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 34 | 0 | 0 | 0 | 0 | 6 | 2 | 0 | 12 | 0 | 0 | 3 | 0 | 1 | 1 | 5 | 0 | 0 | 0 | 0 | 196 | 18 | 0 | 2 | 102 | 1 | 38 | 15 | 0 | 0 | 2 | 0 | 0 | 2 | 9 | 3 | 1 | 2 | 0 | 5 | 0 | 2 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 9 | 0 | 15 | 3 | 0 | 0 | 3 | 0 | 0 | 9 | 0 | 0 | 1 | 29 | 0 | 0 | 0 | 0 | 1 | 107 | 0 | 0 | 0 | 1 | 0 | 0 | 76 | 0 | 8 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 21 | 9 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 19 | 0 | 30 | 1 | 0 | 23 | 33 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 45 | 0 | 0 | 7 | 0 | 0 | 0 | 1 | 1278 |
| 2004 | 88 | 0 | 67 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 37 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 108 | 17 | 0 | 0 | 323 | 0 | 19 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 62 | 1 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 67 | 0 | 0 | 0 | 0 | 3 | 32 | 0 | 0 | 0 | 0 | 0 | 0 | 44 | 0 | 17 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 33 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 44 | 0 | 0 | 0 | 27 | 0 | 10 | 0 | 0 | 5 | 9 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 34 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1166 |
| 2005 | 155 | 0 | 104 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 42 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 33 | 0 | 0 | 0 | 0 | 6 | 3 | 0 | 6 | 0 | 0 | 0 | 1 | 4 | 0 | 1 | 0 | 1 | 0 | 0 | 146 | 15 | 0 | 5 | 617 | 0 | 43 | 6 | 1 | 0 | 0 | 4 | 0 | 2 | 8 | 2 | 2 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 8 | 0 | 70 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 77 | 0 | 0 | 0 | 0 | 2 | 25 | 0 | 0 | 1 | 0 | 0 | 0 | 64 | 1 | 1 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 133 | 0 | 0 | 6 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 155 | 1 | 2 | 0 | 41 | 0 | 11 | 0 | 0 | 29 | 21 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 2017 |
| 2006 | 282 | 0 | 152 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 23 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 5 | 0 | 0 | 2 | 1 | 11 | 1 | 0 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 34 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 23 | 0 | 0 | 4 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 167 | 10 | 0 | 14 | 838 | 1 | 81 | 4 | 3 | 0 | 0 | 1 | 0 | 2 | 6 | 0 | 3 | 0 | 0 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 0 | 0 | 2 | 0 | 83 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 0 | 1 | 164 | 0 | 0 | 0 | 0 | 1 | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 0 | 3 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 1 | 0 | 0 | 0 | 0 | 23 | 217 | 0 | 0 | 24 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 202 | 0 | 0 | 0 | 43 | 0 | 0 | 0 | 0 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 2758 |
| 2007 | 341 | 0 | 124 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 9 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 1 | 7 | 8 | 0 | 30 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 0 | 0 | 1 | 16 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 149 | 2 | 0 | 9 | 1047 | 1 | 53 | 0 | 1 | 1 | 0 | 0 | 0 | 12 | 5 | 0 | 1 | 0 | 1 | 18 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 10 | 0 | 1 | 6 | 1 | 3 | 0 | 90 | 0 | 0 | 0 | 0 | 0 | 7 | 61 | 0 | 0 | 0 | 260 | 0 | 1 | 0 | 0 | 4 | 65 | 0 | 0 | 0 | 0 | 0 | 0 | 51 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 156 | 2 | 0 | 0 | 0 | 0 | 0 | 11 | 132 | 0 | 0 | 23 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 293 | 0 | 0 | 1 | 29 | 0 | 1 | 1 | 0 | 20 | 8 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 88 | 0 | 1 | 7 | 0 | 0 | 0 | 1 | 3242 |
| 2008 | 414 | 0 | 107 | 0 | 0 | 0 | 0 | 0 | 3 | 7 | 2 | 0 | 1 | 19 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 4 | 0 | 0 | 0 | 2 | 0 | 7 | 2 | 1 | 5 | 2 | 8 | 4 | 20 | 133 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 20 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 2 | 0 | 8 | 0 | 0 | 1 | 13 | 0 | 0 | 0 | 0 | 33 | 3 | 0 | 53 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 2 | 0 | 534 | 13 | 0 | 8 | 1106 | 5 | 134 | 2 | 0 | 0 | 3 | 0 | 1 | 11 | 11 | 0 | 0 | 0 | 0 | 58 | 0 | 1 | 1 | 0 | 0 | 0 | 9 | 0 | 0 | 2 | 1 | 9 | 0 | 0 | 2 | 0 | 8 | 0 | 0 | 0 | 0 | 20 | 0 | 120 | 1 | 0 | 0 | 5 | 0 | 9 | 76 | 0 | 0 | 0 | 568 | 0 | 0 | 0 | 0 | 1 | 276 | 0 | 0 | 0 | 0 | 0 | 1 | 170 | 1 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 172 | 1 | 0 | 0 | 0 | 0 | 0 | 37 | 100 | 0 | 0 | 32 | 0 | 3 | 1 | 0 | 1 | 1 | 0 | 1 | 200 | 0 | 0 | 2 | 32 | 0 | 7 | 1 | 0 | 39 | 18 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 63 | 0 | 0 | 22 | 0 | 0 | 0 | 5 | 4805 |
| 2009 | 503 | 1 | 108 | 0 | 1 | 0 | 1 | 1 | 1 | 3 | 2 | 0 | 0 | 27 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 5 | 3 | 8 | 7 | 139 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 54 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 25 | 4 | 0 | 115 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 | 0 | 1 | 0 | 672 | 19 | 0 | 15 | 1137 | 0 | 36 | 4 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 14 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 6 | 0 | 0 | 5 | 0 | 1 | 1 | 0 | 0 | 0 | 14 | 0 | 39 | 1 | 0 | 0 | 0 | 0 | 4 | 42 | 0 | 0 | 0 | 667 | 1 | 0 | 0 | 0 | 4 | 229 | 0 | 0 | 0 | 0 | 0 | 0 | 152 | 1 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 122 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 37 | 0 | 0 | 26 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 298 | 0 | 0 | 0 | 13 | 0 | 2 | 2 | 0 | 23 | 11 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 10 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 4721 |
| 2010 | 542 | 0 | 100 | 0 | 2 | 0 | 5 | 0 | 1 | 0 | 0 | 0 | 2 | 22 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 29 | 0 | 3 | 2 | 13 | 1 | 5 | 1 | 136 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 663 | 4 | 0 | 14 | 1179 | 4 | 14 | 10 | 7 | 0 | 0 | 2 | 0 | 12 | 1 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 11 | 0 | 38 | 1 | 0 | 0 | 0 | 0 | 3 | 63 | 0 | 0 | 1 | 713 | 0 | 0 | 2 | 0 | 0 | 205 | 1 | 0 | 0 | 0 | 0 | 0 | 251 | 8 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 130 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 28 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 1 | 253 | 0 | 1 | 0 | 20 | 0 | 6 | 4 | 2 | 57 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 112 | 0 | 0 | 0 | 1 | 4826 |
| 2011 | 421 | 0 | 15 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 13 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 8 | 0 | 3 | 0 | 3 | 0 | 6 | 4 | 94 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 18 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 3 | 8 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 645 | 21 | 0 | 13 | 1308 | 4 | 51 | 5 | 3 | 0 | 0 | 0 | 5 | 42 | 1 | 1 | 1 | 0 | 0 | 10 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 3 | 0 | 2 | 0 | 0 | 1 | 0 | 2 | 0 | 46 | 2 | 0 | 0 | 0 | 0 | 2 | 175 | 0 | 0 | 3 | 1012 | 0 | 0 | 4 | 0 | 0 | 151 | 0 | 2 | 0 | 0 | 0 | 0 | 188 | 2 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 1 | 3 | 49 | 0 | 0 | 0 | 182 | 0 | 0 | 3 | 51 | 0 | 0 | 3 | 0 | 47 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 0 | 118 | 0 | 0 | 0 | 1 | 5076 |
| 2012 | 1469 | 0 | 41 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 26 | 18 | 0 | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 4 | 0 | 0 | 3 | 4 | 0 | 2 | 4 | 115 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 49 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 66 | 0 | 0 | 0 | 0 | 3 | 5 | 0 | 22 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 611 | 39 | 0 | 5 | 1437 | 29 | 65 | 10 | 16 | 0 | 0 | 2 | 4 | 80 | 4 | 0 | 0 | 1 | 0 | 15 | 0 | 1 | 57 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 1 | 19 | 0 | 0 | 0 | 0 | 16 | 1 | 0 | 0 | 1 | 17 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 1 | 616 | 0 | 0 | 0 | 1654 | 0 | 0 | 4 | 0 | 6 | 249 | 0 | 0 | 0 | 0 | 0 | 0 | 151 | 6 | 6 | 12 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 325 | 4 | 0 | 5 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 40 | 0 | 0 | 2 | 0 | 180 | 0 | 5 | 0 | 279 | 0 | 0 | 3 | 188 | 0 | 4 | 8 | 0 | 55 | 20 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 23 | 0 | 0 | 312 | 0 | 0 | 0 | 0 | 8522 |
| 2013 | 1443 | 1 | 22 | 0 | 0 | 0 | 2 | 1 | 1 | 1 | 0 | 1 | 52 | 139 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 3 | 1 | 2 | 2 | 3 | 4 | 20 | 0 | 5 | 12 | 149 | 0 | 0 | 2 | 0 | 9 | 1 | 0 | 22 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 321 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 18 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 54 | 0 | 0 | 6 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 694 | 32 | 0 | 11 | 2852 | 27 | 37 | 7 | 4 | 0 | 1 | 1 | 4 | 80 | 6 | 0 | 0 | 0 | 0 | 121 | 0 | 0 | 291 | 0 | 0 | 0 | 0 | 2 | 0 | 13 | 3 | 58 | 0 | 0 | 0 | 0 | 8 | 0 | 2 | 0 | 18 | 18 | 0 | 104 | 0 | 0 | 0 | 0 | 0 | 4 | 346 | 0 | 0 | 0 | 2215 | 0 | 0 | 10 | 0 | 10 | 651 | 0 | 0 | 0 | 1 | 0 | 0 | 144 | 4 | 6 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 342 | 13 | 0 | 13 | 0 | 0 | 0 | 5 | 14 | 0 | 0 | 46 | 0 | 0 | 0 | 2 | 285 | 2 | 0 | 7 | 472 | 0 | 3 | 29 | 42 | 0 | 0 | 5 | 1 | 137 | 20 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 64 | 0 | 0 | 425 | 0 | 0 | 0 | 3 | 12036 |
| 2014 | 1824 | 2 | 13 | 0 | 0 | 0 | 1 | 0 | 8 | 0 | 3 | 0 | 42 | 130 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 5 | 0 | 68 | 3 | 94 | 1 | 17 | 37 | 231 | 0 | 0 | 0 | 0 | 5 | 5 | 0 | 110 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 354 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 2 | 13 | 1 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 860 | 35 | 0 | 9 | 3933 | 33 | 293 | 7 | 1 | 1 | 5 | 3 | 0 | 115 | 4 | 0 | 2 | 0 | 0 | 204 | 0 | 1 | 728 | 0 | 0 | 0 | 3 | 1 | 0 | 12 | 9 | 69 | 1 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 22 | 15 | 0 | 7 | 1 | 0 | 0 | 1 | 2 | 5 | 714 | 0 | 0 | 0 | 2151 | 0 | 0 | 15 | 0 | 12 | 597 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 2 | 14 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 872 | 20 | 0 | 40 | 0 | 0 | 0 | 4 | 16 | 0 | 0 | 157 | 0 | 0 | 5 | 0 | 331 | 1 | 1 | 11 | 423 | 0 | 0 | 23 | 94 | 1 | 6 | 895 | 2 | 103 | 29 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 133 | 0 | 0 | 763 | 0 | 0 | 0 | 1 | 16903 |
| 2015 | 1928 | 4 | 16 | 0 | 0 | 0 | 1 | 2 | 14 | 0 | 0 | 0 | 18 | 469 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 5 | 0 | 2 | 6 | 102 | 0 | 83 | 5 | 48 | 27 | 1 | 17 | 136 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 143 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 647 | 0 | 0 | 0 | 2 | 7 | 0 | 0 | 9 | 37 | 0 | 0 | 0 | 0 | 1 | 65 | 0 | 31 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 884 | 29 | 0 | 9 | 2751 | 28 | 58 | 5 | 4 | 0 | 10 | 4 | 0 | 71 | 2 | 1 | 3 | 2 | 0 | 44 | 1 | 0 | 543 | 0 | 0 | 0 | 4 | 0 | 0 | 5 | 2 | 121 | 1 | 0 | 0 | 0 | 19 | 0 | 1 | 1 | 8 | 37 | 0 | 48 | 3 | 0 | 0 | 0 | 0 | 41 | 638 | 0 | 0 | 0 | 1243 | 0 | 0 | 19 | 0 | 10 | 721 | 0 | 0 | 1 | 0 | 0 | 0 | 21 | 0 | 103 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 418 | 5 | 1 | 54 | 0 | 0 | 0 | 1 | 11 | 0 | 0 | 158 | 0 | 0 | 32 | 0 | 491 | 0 | 3 | 14 | 278 | 0 | 1 | 17 | 422 | 0 | 10 | 637 | 0 | 114 | 38 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 246 | 0 | 0 | 664 | 0 | 0 | 0 | 0 | 14965 |
| 2016 | 1617 | 2 | 9 | 0 | 2 | 0 | 2 | 2 | 9 | 3 | 2 | 0 | 3 | 88 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 10 | 83 | 0 | 56 | 6 | 38 | 5 | 18 | 5 | 109 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 169 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 376 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 3 | 26 | 0 | 0 | 0 | 0 | 4 | 44 | 1 | 31 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1025 | 19 | 0 | 10 | 3360 | 15 | 50 | 11 | 1 | 0 | 1 | 9 | 3 | 65 | 8 | 2 | 4 | 4 | 0 | 41 | 0 | 0 | 422 | 0 | 0 | 0 | 0 | 1 | 0 | 19 | 1 | 99 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 79 | 74 | 0 | 43 | 6 | 0 | 0 | 1 | 0 | 25 | 533 | 0 | 0 | 0 | 864 | 1 | 0 | 15 | 0 | 3 | 632 | 2 | 0 | 0 | 15 | 0 | 0 | 55 | 1 | 124 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 602 | 27 | 1 | 59 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 173 | 0 | 0 | 16 | 1 | 473 | 1 | 2 | 3 | 329 | 0 | 0 | 12 | 542 | 0 | 15 | 61 | 0 | 105 | 64 | 1 | 0 | 0 | 0 | 6 | 0 | 0 | 157 | 0 | 0 | 525 | 0 | 0 | 0 | 0 | 13587 |
| 2017 | 1414 | 1 | 14 | 0 | 6 | 0 | 3 | 0 | 4 | 1 | 2 | 0 | 21 | 41 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 32 | 21 | 0 | 94 | 12 | 43 | 6 | 12 | 6 | 117 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 143 | 0 | 1 | 0 | 1 | 0 | 0 | 3 | 224 | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 2 | 41 | 0 | 0 | 4 | 0 | 1 | 27 | 0 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 966 | 27 | 0 | 11 | 2466 | 17 | 32 | 8 | 13 | 2 | 0 | 8 | 0 | 97 | 8 | 0 | 2 | 1 | 1 | 23 | 0 | 1 | 190 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 2 | 141 | 3 | 0 | 0 | 0 | 20 | 0 | 0 | 0 | 11 | 115 | 0 | 247 | 1 | 0 | 0 | 0 | 0 | 13 | 484 | 0 | 0 | 1 | 719 | 0 | 8 | 10 | 0 | 8 | 692 | 1 | 0 | 0 | 0 | 0 | 0 | 33 | 2 | 54 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 614 | 29 | 0 | 54 | 0 | 0 | 0 | 4 | 41 | 0 | 0 | 106 | 0 | 0 | 18 | 0 | 243 | 1 | 2 | 6 | 179 | 0 | 0 | 4 | 181 | 0 | 13 | 61 | 0 | 122 | 65 | 0 | 0 | 0 | 0 | 17 | 2 | 0 | 83 | 0 | 0 | 226 | 0 | 0 | 2 | 3 | 10900 |
| [sum_per_year] | 12731 | 80 | 2743 | 1 | 499 | 1 | 815 | 24 | 114 | 115 | 49 | 5 | 207 | 1648 | 3 | 13 | 154 | 8 | 8 | 6 | 314 | 159 | 10 | 273 | 6 | 52 | 52 | 613 | 259 | 332 | 96 | 283 | 91 | 2365 | 252 | 8306 | 5 | 67 | 57 | 30 | 132 | 32 | 10 | 775 | 41 | 22 | 3 | 90 | 38 | 10 | 220 | 2479 | 5320 | 2 | 10 | 16 | 190 | 1 | 17 | 20 | 2693 | 7 | 3 | 8 | 3 | 217 | 735 | 19 | 1275 | 5 | 56 | 2050 | 25 | 9 | 26 | 213 | 323 | 26 | 46 | 4 | 11960 | 761 | 1 | 684 | 24636 | 307 | 2183 | 1565 | 74 | 36 | 402 | 113 | 27 | 683 | 196 | 76 | 35 | 27 | 17 | 2478 | 29 | 34 | 2249 | 8 | 16 | 33 | 118 | 27 | 5 | 99 | 22 | 566 | 23 | 12 | 18 | 2 | 524 | 21 | 5 | 36 | 363 | 546 | 151 | 1215 | 130 | 31 | 1 | 20 | 1970 | 154 | 3907 | 1 | 6 | 19 | 14368 | 127 | 89 | 114 | 4 | 6096 | 6908 | 39 | 140 | 7 | 36 | 83 | 6 | 2194 | 159 | 371 | 118 | 12 | 11 | 2 | 101 | 7 | 18 | 6 | 4 | 4142 | 2016 | 38 | 225 | 1 | 2 | 78 | 3249 | 3022 | 2 | 1 | 967 | 66 | 16 | 132 | 111 | 2201 | 50 | 188 | 59 | 3849 | 48 | 22 | 109 | 4292 | 2 | 394 | 1709 | 22 | 5235 | 2836 | 82 | 21 | 2 | 1 | 293 | 12 | 1 | 2227 | 541 | 5 | 3347 | 203 | 50 | 62 | 101 | 181691 |
# sheet view total cacasualties & killed & wounded each country under region
results_terr =df_terr[['Region','Country','Killed','Wounded','casualties']]
results_terr = results_terr.groupby(['Region','Country']).sum().sort_values(by='casualties',ascending=False).reset_index()
results_terr.T
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | Middle East & North Africa | South Asia | South Asia | South Asia | Sub-Saharan Africa | South Asia | Middle East & North Africa | South America | North America | Southeast Asia | Middle East & North Africa | Sub-Saharan Africa | Middle East & North Africa | Central America & Caribbean | South America | Middle East & North Africa | Middle East & North Africa | Central America & Caribbean | Eastern Europe | Southeast Asia | Middle East & North Africa | Western Europe | South Asia | Middle East & North Africa | Sub-Saharan Africa | Sub-Saharan Africa | East Asia | Sub-Saharan Africa | Central America & Caribbean | Western Europe | Sub-Saharan Africa | Middle East & North Africa | Middle East & North Africa | Sub-Saharan Africa | Sub-Saharan Africa | Eastern Europe | Middle East & North Africa | Sub-Saharan Africa | Sub-Saharan Africa | Sub-Saharan Africa | South Asia | Sub-Saharan Africa | Southeast Asia | Sub-Saharan Africa | Western Europe | Sub-Saharan Africa | Sub-Saharan Africa | Southeast Asia | East Asia | Sub-Saharan Africa | Sub-Saharan Africa | Middle East & North Africa | Sub-Saharan Africa | Western Europe | North America | Central Asia | Southeast Asia | South America | Western Europe | South America | Sub-Saharan Africa | Western Europe | Middle East & North Africa | Western Europe | Central Asia | Sub-Saharan Africa | Central America & Caribbean | Sub-Saharan Africa | Western Europe | Central America & Caribbean | Sub-Saharan Africa | North America | Middle East & North Africa | South America | Eastern Europe | Central Asia | Sub-Saharan Africa | Sub-Saharan Africa | Eastern Europe | Middle East & North Africa | Sub-Saharan Africa | Sub-Saharan Africa | Middle East & North Africa | South America | Sub-Saharan Africa | Eastern Europe | Sub-Saharan Africa | Sub-Saharan Africa | Central Asia | Southeast Asia | Eastern Europe | Sub-Saharan Africa | Middle East & North Africa | Eastern Europe | Eastern Europe | Sub-Saharan Africa | Sub-Saharan Africa | South America | Australasia & Oceania | Eastern Europe | Western Europe | Western Europe | Central America & Caribbean | Western Europe | Middle East & North Africa | Western Europe | East Asia | East Asia | South Asia | Australasia & Oceania | South America | Sub-Saharan Africa | South America | Western Europe | Central America & Caribbean | Sub-Saharan Africa | Eastern Europe | Central Asia | Sub-Saharan Africa | East Asia | Western Europe | Eastern Europe | Southeast Asia | Western Europe | Western Europe | Southeast Asia | Sub-Saharan Africa | Central America & Caribbean | Central America & Caribbean | Sub-Saharan Africa | South America | Eastern Europe | Central Asia | Australasia & Oceania | Central America & Caribbean | South America | Central America & Caribbean | Eastern Europe | East Asia | Sub-Saharan Africa | Central America & Caribbean | Eastern Europe | Eastern Europe | Western Europe | Eastern Europe | Central Asia | Eastern Europe | Western Europe | Sub-Saharan Africa | Sub-Saharan Africa | Central America & Caribbean | Southeast Asia | Australasia & Oceania | Eastern Europe | Sub-Saharan Africa | Middle East & North Africa | Eastern Europe | Central America & Caribbean | Western Europe | Southeast Asia | Sub-Saharan Africa | Sub-Saharan Africa | Central America & Caribbean | South Asia | Eastern Europe | South America | Middle East & North Africa | Central America & Caribbean | Australasia & Oceania | Eastern Europe | South America | Eastern Europe | Central America & Caribbean | Sub-Saharan Africa | Sub-Saharan Africa | Eastern Europe | Sub-Saharan Africa | Southeast Asia | East Asia | Central Asia | Western Europe | Middle East & North Africa | Sub-Saharan Africa | Middle East & North Africa | Australasia & Oceania | Eastern Europe | Western Europe | Australasia & Oceania | Eastern Europe | Sub-Saharan Africa | Central America & Caribbean | Middle East & North Africa | Central America & Caribbean | Central America & Caribbean | Eastern Europe | South Asia | Southeast Asia | Western Europe | Western Europe | Australasia & Oceania | Australasia & Oceania | Central America & Caribbean | South America | Sub-Saharan Africa | Australasia & Oceania |
| Country | Iraq | Afghanistan | Pakistan | India | Nigeria | Sri Lanka | Syria | Colombia | United States | Philippines | Algeria | Somalia | Yemen | El Salvador | Peru | Turkey | Lebanon | Nicaragua | Russia | Thailand | Israel | United Kingdom | Bangladesh | Egypt | Kenya | South Africa | Japan | Burundi | Guatemala | Spain | Sudan | Libya | Iran | Angola | Democratic Republic of the Congo | Ukraine | West Bank and Gaza Strip | Mozambique | Uganda | Rwanda | Nepal | South Sudan | Indonesia | Cameroon | France | Ethiopia | Central African Republic | Myanmar | China | Chad | Mali | Saudi Arabia | Niger | Italy | Mexico | Tajikistan | Cambodia | Argentina | Greece | Chile | Sierra Leone | West Germany (FRG) | Tunisia | Germany | Georgia | Senegal | Haiti | Namibia | Belgium | Honduras | Zaire | Canada | Morocco | Venezuela | Kosovo | Azerbaijan | Ivory Coast | Djibouti | Yugoslavia | Jordan | Zimbabwe | Rhodesia | Kuwait | Brazil | Burkina Faso | Croatia | Tanzania | Guinea | Uzbekistan | Malaysia | Soviet Union | Republic of the Congo | Bahrain | Bosnia-Herzegovina | Belarus | Madagascar | Liberia | Bolivia | Papua New Guinea | Albania | Switzerland | Norway | Dominican Republic | Austria | United Arab Emirates | Ireland | Taiwan | South Korea | Maldives | Australia | Ecuador | Zambia | Paraguay | Portugal | Panama | Togo | Macedonia | Armenia | Eritrea | Hong Kong | Sweden | Moldova | Laos | Netherlands | Cyprus | South Vietnam | Lesotho | Barbados | Jamaica | Mauritania | Guyana | Bulgaria | Kazakhstan | New Caledonia | Costa Rica | Suriname | Guadeloupe | Czechoslovakia | Macau | Guinea-Bissau | Trinidad and Tobago | Poland | East Germany (GDR) | Finland | Latvia | Kyrgyzstan | Czech Republic | Denmark | Malawi | Ghana | Grenada | Vietnam | Fiji | Hungary | Botswana | Qatar | Slovak Republic | Cuba | Malta | East Timor | People's Republic of the Congo | Gambia | St. Lucia | Bhutan | Estonia | French Guiana | International | Dominica | French Polynesia | Romania | Uruguay | Serbia | St. Kitts and Nevis | Swaziland | Gabon | Serbia-Montenegro | Benin | Singapore | North Korea | Turkmenistan | Luxembourg | Western Sahara | Equatorial Guinea | North Yemen | Solomon Islands | Lithuania | Vatican City | New Zealand | Slovenia | Comoros | Belize | South Yemen | Martinique | Bahamas | Montenegro | Mauritius | Brunei | Andorra | Iceland | New Hebrides | Vanuatu | Antigua and Barbuda | Falkland Islands | Seychelles | Wallis and Futuna |
| Killed | 78589.0 | 39384.0 | 23822.0 | 19341.0 | 22682.0 | 15530.0 | 15229.0 | 14698.0 | 3771.0 | 9559.0 | 11066.0 | 10273.0 | 8776.0 | 12053.0 | 12771.0 | 6888.0 | 4061.0 | 10598.0 | 4308.0 | 2742.0 | 1703.0 | 3410.0 | 1244.0 | 3869.0 | 1948.0 | 2674.0 | 66.0 | 4205.0 | 5167.0 | 1288.0 | 3883.0 | 2598.0 | 1673.0 | 3043.0 | 4069.0 | 2261.0 | 1500.0 | 2711.0 | 3065.0 | 3236.0 | 1969.0 | 2634.0 | 1238.0 | 2347.0 | 534.0 | 1765.0 | 1990.0 | 1280.0 | 1008.0 | 1119.0 | 1432.0 | 672.0 | 1474.0 | 420.0 | 780.0 | 307.0 | 543.0 | 490.0 | 325.0 | 228.0 | 840.0 | 97.0 | 351.0 | 84.0 | 278.0 | 325.0 | 336.0 | 220.0 | 79.0 | 307.0 | 324.0 | 365.0 | 292.0 | 227.0 | 83.0 | 258.0 | 268.0 | 274.0 | 119.0 | 133.0 | 154.0 | 217.0 | 63.0 | 203.0 | 134.0 | 248.0 | 73.0 | 213.0 | 68.0 | 152.0 | 96.0 | 182.0 | 44.0 | 79.0 | 14.0 | 31.0 | 177.0 | 42.0 | 79.0 | 42.0 | 74.0 | 79.0 | 34.0 | 30.0 | 123.0 | 117.0 | 60.0 | 10.0 | 20.0 | 23.0 | 54.0 | 70.0 | 59.0 | 32.0 | 38.0 | 76.0 | 49.0 | 37.0 | 46.0 | 4.0 | 22.0 | 13.0 | 27.0 | 37.0 | 45.0 | 81.0 | 46.0 | 76.0 | 42.0 | 43.0 | 41.0 | 28.0 | 39.0 | 35.0 | 17.0 | 29.0 | 8.0 | 27.0 | 1.0 | 17.0 | 6.0 | 9.0 | 2.0 | 11.0 | 2.0 | 10.0 | 6.0 | 5.0 | 33.0 | 19.0 | 9.0 | 1.0 | 8.0 | 6.0 | 11.0 | 7.0 | 7.0 | 8.0 | 5.0 | 9.0 | 15.0 | 13.0 | 2.0 | 9.0 | 3.0 | 1.0 | 1.0 | 3.0 | 0.0 | 4.0 | 6.0 | 3.0 | 0.0 | 6.0 | 6.0 | 3.0 | 0.0 | 5.0 | 3.0 | 3.0 | 0.0 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 0.0 | 1.0 | 1.0 | 1.0 | 3.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Wounded | 134690.0 | 44277.0 | 42038.0 | 28980.0 | 10239.0 | 15561.0 | 14109.0 | 10328.0 | 20702.0 | 13367.0 | 9150.0 | 8875.0 | 9328.0 | 5062.0 | 4078.0 | 9899.0 | 10904.0 | 1731.0 | 7441.0 | 7818.0 | 7946.0 | 6106.0 | 8225.0 | 4822.0 | 6263.0 | 4545.0 | 6998.0 | 2454.0 | 1231.0 | 4935.0 | 2169.0 | 3310.0 | 4029.0 | 2455.0 | 1369.0 | 2841.0 | 3014.0 | 1518.0 | 1145.0 | 922.0 | 2151.0 | 1323.0 | 2445.0 | 1063.0 | 2516.0 | 1214.0 | 942.0 | 1631.0 | 1842.0 | 1681.0 | 1356.0 | 1666.0 | 472.0 | 1291.0 | 683.0 | 1109.0 | 786.0 | 755.0 | 733.0 | 755.0 | 122.0 | 862.0 | 433.0 | 683.0 | 392.0 | 323.0 | 300.0 | 406.0 | 516.0 | 240.0 | 211.0 | 146.0 | 200.0 | 247.0 | 365.0 | 187.0 | 171.0 | 162.0 | 281.0 | 260.0 | 222.0 | 158.0 | 300.0 | 160.0 | 219.0 | 73.0 | 234.0 | 57.0 | 200.0 | 101.0 | 150.0 | 54.0 | 189.0 | 150.0 | 212.0 | 185.0 | 36.0 | 166.0 | 91.0 | 126.0 | 94.0 | 88.0 | 124.0 | 126.0 | 27.0 | 31.0 | 87.0 | 134.0 | 122.0 | 113.0 | 79.0 | 62.0 | 69.0 | 95.0 | 83.0 | 34.0 | 60.0 | 71.0 | 62.0 | 102.0 | 80.0 | 88.0 | 73.0 | 58.0 | 41.0 | 0.0 | 34.0 | 3.0 | 36.0 | 28.0 | 24.0 | 36.0 | 21.0 | 23.0 | 38.0 | 24.0 | 45.0 | 23.0 | 46.0 | 27.0 | 35.0 | 31.0 | 37.0 | 27.0 | 36.0 | 26.0 | 29.0 | 29.0 | 0.0 | 13.0 | 21.0 | 27.0 | 18.0 | 17.0 | 11.0 | 13.0 | 11.0 | 10.0 | 12.0 | 7.0 | 0.0 | 2.0 | 12.0 | 5.0 | 11.0 | 13.0 | 12.0 | 10.0 | 13.0 | 9.0 | 6.0 | 9.0 | 9.0 | 3.0 | 3.0 | 5.0 | 8.0 | 3.0 | 4.0 | 3.0 | 6.0 | 4.0 | 3.0 | 1.0 | 0.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2.0 | 0.0 | 2.0 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| casualties | 213279.0 | 83661.0 | 65860.0 | 48321.0 | 32921.0 | 31091.0 | 29338.0 | 25026.0 | 24473.0 | 22926.0 | 20216.0 | 19148.0 | 18104.0 | 17115.0 | 16849.0 | 16787.0 | 14965.0 | 12329.0 | 11749.0 | 10560.0 | 9649.0 | 9516.0 | 9469.0 | 8691.0 | 8211.0 | 7219.0 | 7064.0 | 6659.0 | 6398.0 | 6223.0 | 6052.0 | 5908.0 | 5702.0 | 5498.0 | 5438.0 | 5102.0 | 4514.0 | 4229.0 | 4210.0 | 4158.0 | 4120.0 | 3957.0 | 3683.0 | 3410.0 | 3050.0 | 2979.0 | 2932.0 | 2911.0 | 2850.0 | 2800.0 | 2788.0 | 2338.0 | 1946.0 | 1711.0 | 1463.0 | 1416.0 | 1329.0 | 1245.0 | 1058.0 | 983.0 | 962.0 | 959.0 | 784.0 | 767.0 | 670.0 | 648.0 | 636.0 | 626.0 | 595.0 | 547.0 | 535.0 | 511.0 | 492.0 | 474.0 | 448.0 | 445.0 | 439.0 | 436.0 | 400.0 | 393.0 | 376.0 | 375.0 | 363.0 | 363.0 | 353.0 | 321.0 | 307.0 | 270.0 | 268.0 | 253.0 | 246.0 | 236.0 | 233.0 | 229.0 | 226.0 | 216.0 | 213.0 | 208.0 | 170.0 | 168.0 | 168.0 | 167.0 | 158.0 | 156.0 | 150.0 | 148.0 | 147.0 | 144.0 | 142.0 | 136.0 | 133.0 | 132.0 | 128.0 | 127.0 | 121.0 | 110.0 | 109.0 | 108.0 | 108.0 | 106.0 | 102.0 | 101.0 | 100.0 | 95.0 | 86.0 | 81.0 | 80.0 | 79.0 | 78.0 | 71.0 | 65.0 | 64.0 | 60.0 | 58.0 | 55.0 | 53.0 | 53.0 | 50.0 | 47.0 | 44.0 | 41.0 | 40.0 | 39.0 | 38.0 | 38.0 | 36.0 | 35.0 | 34.0 | 33.0 | 32.0 | 30.0 | 28.0 | 26.0 | 23.0 | 22.0 | 20.0 | 18.0 | 18.0 | 17.0 | 16.0 | 15.0 | 15.0 | 14.0 | 14.0 | 14.0 | 14.0 | 13.0 | 13.0 | 13.0 | 13.0 | 12.0 | 12.0 | 9.0 | 9.0 | 9.0 | 8.0 | 8.0 | 8.0 | 7.0 | 6.0 | 6.0 | 5.0 | 5.0 | 4.0 | 4.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
# killed per year
k = df_terr[["Year", "Killed"]].groupby("Year").sum()
# wounded per year
w = df_terr[["Year", "Wounded"]].groupby("Year").sum()
print(w.head())
print(k.head())
Wounded
Year
1970 212.0
1971 82.0
1972 409.0
1973 495.0
1974 865.0
Killed
Year
1970 174.0
1971 173.0
1972 566.0
1973 370.0
1974 539.0
merged_k_w = pd.merge(w, k, on="Year")
merged_k_w.reset_index(inplace=True)
merged_k_w.head(10)
| Year | Wounded | Killed | |
|---|---|---|---|
| 0 | 1970 | 212.0 | 174.0 |
| 1 | 1971 | 82.0 | 173.0 |
| 2 | 1972 | 409.0 | 566.0 |
| 3 | 1973 | 495.0 | 370.0 |
| 4 | 1974 | 865.0 | 539.0 |
| 5 | 1975 | 617.0 | 617.0 |
| 6 | 1976 | 756.0 | 674.0 |
| 7 | 1977 | 518.0 | 456.0 |
| 8 | 1978 | 1600.0 | 1459.0 |
| 9 | 1979 | 2506.0 | 2100.0 |
# Apply style
plt.style.use('ggplot')
# Create a figure and two subplots
fig, (ax0, ax1) = plt.subplots(2, 1, figsize=(15, 10))
# Plot 'Killed' data
k.plot(kind="bar", color='#1f77b4', ax=ax0, legend=True)
ax0.set_title("People Killed in Each Year", fontsize=16)
ax0.set_xlabel("Years", fontsize=12)
ax0.set_ylabel("Number of People Killed", fontsize=12)
ax0.set_xticklabels(k.index, rotation=45, fontsize=10)
ax0.grid(True, which='both', linestyle='--', linewidth=0.7, alpha=0.7)
ax0.tick_params(axis='both', which='major', labelsize=10)
ax0.legend(["Killed"], loc='upper left', bbox_to_anchor=(0, 1))
# Add data labels
for i in ax0.containers:
ax0.bar_label(i, label_type='edge', fontsize=7)
# Plot 'Wounded' data
w.plot(kind="bar", color='#ff7f0e', ax=ax1, legend=True)
ax1.set_title("People Wounded in Each Year", fontsize=16)
ax1.set_xlabel("Years", fontsize=12)
ax1.set_ylabel("Number of People Wounded", fontsize=12)
ax1.set_xticklabels(w.index, rotation=45, fontsize=10)
ax1.grid(True, which='both', linestyle='--', linewidth=0.7, alpha=0.7)
ax1.tick_params(axis='both', which='major', labelsize=10)
ax1.legend(["Wounded"], loc='upper left', bbox_to_anchor=(0, 1))
# Add data labels
for i in ax1.containers:
ax1.bar_label(i, label_type='edge', fontsize=7)
# Adjust layout for better visualization
plt.tight_layout()
# Show the plot
plt.show()
Looking at these annual charts & sheet , we can say that the largest year was killing in
2014, and then it began a gradual decline to2017
# largest 10 years people killed
merged_k_w.nlargest(10,['Killed']).drop('Wounded',axis=1)
| Year | Killed | |
|---|---|---|
| 43 | 2014 | 44490.0 |
| 44 | 2015 | 38853.0 |
| 45 | 2016 | 34871.0 |
| 46 | 2017 | 26445.0 |
| 42 | 2013 | 22273.0 |
| 41 | 2012 | 15497.0 |
| 36 | 2007 | 12824.0 |
| 26 | 1997 | 10924.0 |
| 14 | 1984 | 10450.0 |
| 22 | 1992 | 9742.0 |
2015and then it began a gradual decline to 2017# largest 10 years people Wounded
merged_k_w.nlargest(10,['Wounded']).drop('Killed',axis=1)
| Year | Wounded | |
|---|---|---|
| 44 | 2015 | 44043.0 |
| 43 | 2014 | 41128.0 |
| 45 | 2016 | 40001.0 |
| 42 | 2013 | 37688.0 |
| 41 | 2012 | 25445.0 |
| 46 | 2017 | 24927.0 |
| 30 | 2001 | 22774.0 |
| 36 | 2007 | 22524.0 |
| 38 | 2009 | 19138.0 |
| 37 | 2008 | 18998.0 |
- 0 : Represents failure to perform the operation.
- 1: Represents success to perform the operation.
# sheet demonstrate number of terrorist attscks each countery & extent of the success and failure of these strikes and thier impact on people
# Aggregate the data
coun_stats = df_terr.groupby(['Region','Country', 'success']).agg({
'Killed': 'sum',
'Wounded': 'sum',
'Country': 'count' # Counts the number of occurrences for each (Country, success) pair
}).rename(columns={'Country': 'Num_Attacks'}).reset_index()
# Melt the DataFrame to combine 'Killed' and 'Wounded' into a single 'Types' column
coun_stats_melted = pd.melt(coun_stats, id_vars=['Region','Country', 'Num_Attacks', 'success'], value_vars=['Killed', 'Wounded'], var_name='Types', value_name='Counts_Types')
# Display the melted DataFrame
coun_stats_melted.sort_values(by=['Region', 'Country', 'Types']).reset_index(drop=True).sort_values(by='Counts_Types',ascending=False).reset_index(drop=True)
| Region | Country | Num_Attacks | success | Types | Counts_Types | |
|---|---|---|---|---|---|---|
| 0 | Middle East & North Africa | Iraq | 21861 | 1 | Wounded | 132572.0 |
| 1 | Middle East & North Africa | Iraq | 21861 | 1 | Killed | 73036.0 |
| 2 | South Asia | Afghanistan | 11141 | 1 | Wounded | 41643.0 |
| 3 | South Asia | Pakistan | 12600 | 1 | Wounded | 41132.0 |
| 4 | South Asia | Afghanistan | 11141 | 1 | Killed | 36552.0 |
| 5 | South Asia | India | 10280 | 1 | Wounded | 28373.0 |
| 6 | South Asia | Pakistan | 12600 | 1 | Killed | 23294.0 |
| 7 | Sub-Saharan Africa | Nigeria | 3593 | 1 | Killed | 22228.0 |
| 8 | North America | United States | 2340 | 1 | Wounded | 20634.0 |
| 9 | South Asia | India | 10280 | 1 | Killed | 19119.0 |
| 10 | South Asia | Sri Lanka | 2849 | 1 | Killed | 15377.0 |
| 11 | South Asia | Sri Lanka | 2849 | 1 | Wounded | 15193.0 |
| 12 | Middle East & North Africa | Syria | 2119 | 1 | Killed | 15119.0 |
| 13 | South America | Colombia | 7712 | 1 | Killed | 14381.0 |
| 14 | Middle East & North Africa | Syria | 2119 | 1 | Wounded | 14047.0 |
| 15 | Southeast Asia | Philippines | 5975 | 1 | Wounded | 12855.0 |
| 16 | South America | Peru | 5755 | 1 | Killed | 12631.0 |
| 17 | Central America & Caribbean | El Salvador | 5227 | 1 | Killed | 12004.0 |
| 18 | Middle East & North Africa | Algeria | 2561 | 1 | Killed | 11008.0 |
| 19 | Middle East & North Africa | Lebanon | 2182 | 1 | Wounded | 10653.0 |
| 20 | Central America & Caribbean | Nicaragua | 1939 | 1 | Killed | 10569.0 |
| 21 | Sub-Saharan Africa | Nigeria | 3593 | 1 | Wounded | 10106.0 |
| 22 | South America | Colombia | 7712 | 1 | Wounded | 10017.0 |
| 23 | Sub-Saharan Africa | Somalia | 3804 | 1 | Killed | 9824.0 |
| 24 | Middle East & North Africa | Turkey | 3909 | 1 | Wounded | 9702.0 |
| 25 | Southeast Asia | Philippines | 5975 | 1 | Killed | 9289.0 |
| 26 | Middle East & North Africa | Yemen | 2837 | 1 | Wounded | 8987.0 |
| 27 | Middle East & North Africa | Algeria | 2561 | 1 | Wounded | 8970.0 |
| 28 | Sub-Saharan Africa | Somalia | 3804 | 1 | Wounded | 8511.0 |
| 29 | Middle East & North Africa | Yemen | 2837 | 1 | Killed | 8399.0 |
| 30 | South Asia | Bangladesh | 1519 | 1 | Wounded | 7992.0 |
| 31 | Middle East & North Africa | Israel | 1683 | 1 | Wounded | 7805.0 |
| 32 | Southeast Asia | Thailand | 3626 | 1 | Wounded | 7654.0 |
| 33 | Eastern Europe | Russia | 1810 | 1 | Wounded | 7289.0 |
| 34 | East Asia | Japan | 341 | 1 | Wounded | 6990.0 |
| 35 | Middle East & North Africa | Turkey | 3909 | 1 | Killed | 6705.0 |
| 36 | Sub-Saharan Africa | Kenya | 608 | 1 | Wounded | 6247.0 |
| 37 | Western Europe | United Kingdom | 4206 | 1 | Wounded | 5863.0 |
| 38 | Middle East & North Africa | Iraq | 2775 | 0 | Killed | 5553.0 |
| 39 | Central America & Caribbean | Guatemala | 1936 | 1 | Killed | 5133.0 |
| 40 | Central America & Caribbean | El Salvador | 5227 | 1 | Wounded | 5013.0 |
| 41 | Western Europe | Spain | 2818 | 1 | Wounded | 4674.0 |
| 42 | Middle East & North Africa | Egypt | 2011 | 1 | Wounded | 4639.0 |
| 43 | Sub-Saharan Africa | South Africa | 1877 | 1 | Wounded | 4458.0 |
| 44 | Eastern Europe | Russia | 1810 | 1 | Killed | 4192.0 |
| 45 | Sub-Saharan Africa | Burundi | 590 | 1 | Killed | 4181.0 |
| 46 | Middle East & North Africa | Lebanon | 2182 | 1 | Killed | 3996.0 |
| 47 | Sub-Saharan Africa | Democratic Republic of the Congo | 717 | 1 | Killed | 3991.0 |
| 48 | Middle East & North Africa | Iran | 594 | 1 | Wounded | 3976.0 |
| 49 | South America | Peru | 5755 | 1 | Wounded | 3928.0 |
| 50 | Sub-Saharan Africa | Sudan | 933 | 1 | Killed | 3801.0 |
| 51 | North America | United States | 2340 | 1 | Killed | 3758.0 |
| 52 | Middle East & North Africa | Egypt | 2011 | 1 | Killed | 3588.0 |
| 53 | Western Europe | United Kingdom | 4206 | 1 | Killed | 3300.0 |
| 54 | Sub-Saharan Africa | Rwanda | 154 | 1 | Killed | 3235.0 |
| 55 | Middle East & North Africa | Libya | 1986 | 1 | Wounded | 3162.0 |
| 56 | Sub-Saharan Africa | Uganda | 363 | 1 | Killed | 3057.0 |
| 57 | Sub-Saharan Africa | Angola | 486 | 1 | Killed | 3005.0 |
| 58 | South Asia | Afghanistan | 1590 | 0 | Killed | 2832.0 |
| 59 | Middle East & North Africa | West Bank and Gaza Strip | 1766 | 1 | Wounded | 2808.0 |
| 60 | Eastern Europe | Ukraine | 1529 | 1 | Wounded | 2769.0 |
| 61 | Southeast Asia | Thailand | 3626 | 1 | Killed | 2719.0 |
| 62 | Sub-Saharan Africa | Mozambique | 346 | 1 | Killed | 2689.0 |
| 63 | Sub-Saharan Africa | South Africa | 1877 | 1 | Killed | 2647.0 |
| 64 | South Asia | Afghanistan | 1590 | 0 | Wounded | 2634.0 |
| 65 | Sub-Saharan Africa | South Sudan | 193 | 1 | Killed | 2515.0 |
| 66 | Middle East & North Africa | Libya | 1986 | 1 | Killed | 2507.0 |
| 67 | Western Europe | France | 2481 | 1 | Wounded | 2459.0 |
| 68 | Sub-Saharan Africa | Angola | 486 | 1 | Wounded | 2432.0 |
| 69 | Sub-Saharan Africa | Burundi | 590 | 1 | Wounded | 2432.0 |
| 70 | Southeast Asia | Indonesia | 666 | 1 | Wounded | 2428.0 |
| 71 | Sub-Saharan Africa | Cameroon | 309 | 1 | Killed | 2298.0 |
| 72 | Eastern Europe | Ukraine | 1529 | 1 | Killed | 2255.0 |
| 73 | Sub-Saharan Africa | Sudan | 933 | 1 | Wounded | 2147.0 |
| 74 | Middle East & North Africa | Iraq | 2775 | 0 | Wounded | 2118.0 |
| 75 | South Asia | Nepal | 956 | 1 | Wounded | 2098.0 |
| 76 | Sub-Saharan Africa | Central African Republic | 263 | 1 | Killed | 1983.0 |
| 77 | South Asia | Nepal | 956 | 1 | Killed | 1965.0 |
| 78 | Sub-Saharan Africa | Kenya | 608 | 1 | Killed | 1921.0 |
| 79 | East Asia | China | 220 | 1 | Wounded | 1826.0 |
| 80 | Sub-Saharan Africa | Ethiopia | 181 | 1 | Killed | 1748.0 |
| 81 | Central America & Caribbean | Nicaragua | 1939 | 1 | Wounded | 1706.0 |
| 82 | Sub-Saharan Africa | Chad | 87 | 1 | Wounded | 1681.0 |
| 83 | Middle East & North Africa | Israel | 1683 | 1 | Killed | 1664.0 |
| 84 | Middle East & North Africa | Iran | 594 | 1 | Killed | 1647.0 |
| 85 | Middle East & North Africa | Saudi Arabia | 319 | 1 | Wounded | 1631.0 |
| 86 | Southeast Asia | Myanmar | 509 | 1 | Wounded | 1624.0 |
| 87 | Sub-Saharan Africa | Mozambique | 346 | 1 | Wounded | 1514.0 |
| 88 | Sub-Saharan Africa | Mali | 523 | 1 | Killed | 1412.0 |
| 89 | Sub-Saharan Africa | Democratic Republic of the Congo | 717 | 1 | Wounded | 1365.0 |
| 90 | Sub-Saharan Africa | Niger | 146 | 1 | Killed | 1356.0 |
| 91 | Sub-Saharan Africa | Mali | 523 | 1 | Wounded | 1351.0 |
| 92 | Middle East & North Africa | West Bank and Gaza Strip | 1766 | 1 | Killed | 1322.0 |
| 93 | Sub-Saharan Africa | South Sudan | 193 | 1 | Wounded | 1311.0 |
| 94 | Southeast Asia | Myanmar | 509 | 1 | Killed | 1260.0 |
| 95 | Western Europe | Spain | 2818 | 1 | Killed | 1254.0 |
| 96 | Western Europe | Italy | 1392 | 1 | Wounded | 1235.0 |
| 97 | South Asia | Bangladesh | 1519 | 1 | Killed | 1227.0 |
| 98 | Southeast Asia | Indonesia | 666 | 1 | Killed | 1227.0 |
| 99 | Sub-Saharan Africa | Ethiopia | 181 | 1 | Wounded | 1212.0 |
| 100 | Central America & Caribbean | Guatemala | 1936 | 1 | Wounded | 1178.0 |
| 101 | Sub-Saharan Africa | Uganda | 363 | 1 | Wounded | 1111.0 |
| 102 | Central Asia | Tajikistan | 180 | 1 | Wounded | 1103.0 |
| 103 | Sub-Saharan Africa | Chad | 87 | 1 | Killed | 1102.0 |
| 104 | Sub-Saharan Africa | Cameroon | 309 | 1 | Wounded | 1063.0 |
| 105 | East Asia | China | 220 | 1 | Killed | 1002.0 |
| 106 | Sub-Saharan Africa | Central African Republic | 263 | 1 | Wounded | 931.0 |
| 107 | Sub-Saharan Africa | Rwanda | 154 | 1 | Wounded | 921.0 |
| 108 | South Asia | Pakistan | 1768 | 0 | Wounded | 906.0 |
| 109 | Western Europe | West Germany (FRG) | 465 | 1 | Wounded | 847.0 |
| 110 | Sub-Saharan Africa | Sierra Leone | 98 | 1 | Killed | 833.0 |
| 111 | Southeast Asia | Cambodia | 243 | 1 | Wounded | 782.0 |
| 112 | North America | Mexico | 479 | 1 | Killed | 759.0 |
| 113 | South America | Argentina | 714 | 1 | Wounded | 717.0 |
| 114 | Western Europe | Greece | 1126 | 1 | Wounded | 707.0 |
| 115 | South America | Chile | 2221 | 1 | Wounded | 697.0 |
| 116 | Western Europe | Germany | 659 | 1 | Wounded | 665.0 |
| 117 | North America | Mexico | 479 | 1 | Wounded | 660.0 |
| 118 | Middle East & North Africa | Saudi Arabia | 319 | 1 | Killed | 631.0 |
| 119 | South Asia | India | 1680 | 0 | Wounded | 607.0 |
| 120 | Southeast Asia | Cambodia | 243 | 1 | Killed | 542.0 |
| 121 | South Asia | Pakistan | 1768 | 0 | Killed | 528.0 |
| 122 | Western Europe | France | 2481 | 1 | Killed | 513.0 |
| 123 | Southeast Asia | Philippines | 933 | 0 | Wounded | 512.0 |
| 124 | Western Europe | Belgium | 125 | 1 | Wounded | 512.0 |
| 125 | South America | Argentina | 714 | 1 | Killed | 484.0 |
| 126 | Sub-Saharan Africa | Niger | 146 | 1 | Wounded | 467.0 |
| 127 | Sub-Saharan Africa | Nigeria | 314 | 0 | Killed | 454.0 |
| 128 | Sub-Saharan Africa | Somalia | 338 | 0 | Killed | 449.0 |
| 129 | Middle East & North Africa | Tunisia | 97 | 1 | Wounded | 431.0 |
| 130 | Western Europe | Italy | 1392 | 1 | Killed | 407.0 |
| 131 | Sub-Saharan Africa | Namibia | 147 | 1 | Wounded | 405.0 |
| 132 | Middle East & North Africa | Yemen | 510 | 0 | Killed | 377.0 |
| 133 | Central Asia | Georgia | 192 | 1 | Wounded | 372.0 |
| 134 | South Asia | Sri Lanka | 173 | 0 | Wounded | 368.0 |
| 135 | North America | Canada | 75 | 1 | Killed | 365.0 |
| 136 | Sub-Saharan Africa | Somalia | 338 | 0 | Wounded | 364.0 |
| 137 | Eastern Europe | Kosovo | 172 | 1 | Wounded | 357.0 |
| 138 | Middle East & North Africa | Yemen | 510 | 0 | Wounded | 341.0 |
| 139 | Middle East & North Africa | Tunisia | 97 | 1 | Killed | 337.0 |
| 140 | Central America & Caribbean | Haiti | 185 | 1 | Killed | 335.0 |
| 141 | Sub-Saharan Africa | Senegal | 116 | 1 | Killed | 325.0 |
| 142 | Sub-Saharan Africa | Zaire | 45 | 1 | Killed | 324.0 |
| 143 | Sub-Saharan Africa | Senegal | 116 | 1 | Wounded | 322.0 |
| 144 | Western Europe | Greece | 1126 | 1 | Killed | 321.0 |
| 145 | South America | Colombia | 594 | 0 | Killed | 317.0 |
| 146 | South America | Colombia | 594 | 0 | Wounded | 311.0 |
| 147 | Central Asia | Tajikistan | 180 | 1 | Killed | 305.0 |
| 148 | Central America & Caribbean | Honduras | 286 | 1 | Killed | 304.0 |
| 149 | Middle East & North Africa | Morocco | 33 | 1 | Killed | 292.0 |
| 150 | Middle East & North Africa | Kuwait | 63 | 1 | Wounded | 291.0 |
| 151 | Central America & Caribbean | Haiti | 185 | 1 | Wounded | 282.0 |
| 152 | Middle East & North Africa | Egypt | 468 | 0 | Killed | 281.0 |
| 153 | Eastern Europe | Yugoslavia | 179 | 1 | Wounded | 274.0 |
| 154 | Sub-Saharan Africa | Djibouti | 22 | 1 | Killed | 274.0 |
| 155 | Central Asia | Georgia | 192 | 1 | Killed | 272.0 |
| 156 | Southeast Asia | Philippines | 933 | 0 | Killed | 270.0 |
| 157 | Western Europe | Spain | 431 | 0 | Wounded | 261.0 |
| 158 | Central Asia | Azerbaijan | 44 | 1 | Killed | 257.0 |
| 159 | Sub-Saharan Africa | Ivory Coast | 67 | 1 | Killed | 253.0 |
| 160 | Middle East & North Africa | Jordan | 84 | 1 | Wounded | 251.0 |
| 161 | Middle East & North Africa | Lebanon | 296 | 0 | Wounded | 251.0 |
| 162 | Eastern Europe | Croatia | 55 | 1 | Killed | 248.0 |
| 163 | Western Europe | United Kingdom | 1029 | 0 | Wounded | 243.0 |
| 164 | South Asia | Bangladesh | 129 | 0 | Wounded | 233.0 |
| 165 | South America | Venezuela | 246 | 1 | Wounded | 232.0 |
| 166 | Sub-Saharan Africa | Tanzania | 50 | 1 | Wounded | 232.0 |
| 167 | South Asia | India | 1680 | 0 | Killed | 222.0 |
| 168 | South America | Venezuela | 246 | 1 | Killed | 222.0 |
| 169 | Central America & Caribbean | Honduras | 286 | 1 | Wounded | 221.0 |
| 170 | Sub-Saharan Africa | Zimbabwe | 96 | 1 | Wounded | 220.0 |
| 171 | Sub-Saharan Africa | Namibia | 147 | 1 | Killed | 220.0 |
| 172 | Sub-Saharan Africa | Burkina Faso | 45 | 1 | Wounded | 218.0 |
| 173 | Sub-Saharan Africa | Rhodesia | 80 | 1 | Killed | 217.0 |
| 174 | Sub-Saharan Africa | Guinea | 21 | 1 | Killed | 213.0 |
| 175 | Eastern Europe | Belarus | 13 | 1 | Wounded | 212.0 |
| 176 | Sub-Saharan Africa | Zaire | 45 | 1 | Wounded | 211.0 |
| 177 | South America | Chile | 2221 | 1 | Killed | 207.0 |
| 178 | Middle East & North Africa | West Bank and Gaza Strip | 461 | 0 | Wounded | 206.0 |
| 179 | South America | Brazil | 237 | 1 | Killed | 201.0 |
| 180 | Central Asia | Uzbekistan | 19 | 1 | Wounded | 200.0 |
| 181 | Middle East & North Africa | Morocco | 33 | 1 | Wounded | 199.0 |
| 182 | Middle East & North Africa | Turkey | 383 | 0 | Wounded | 197.0 |
| 183 | Middle East & North Africa | Bahrain | 179 | 1 | Wounded | 188.0 |
| 184 | Middle East & North Africa | Egypt | 468 | 0 | Wounded | 183.0 |
| 185 | Middle East & North Africa | Turkey | 383 | 0 | Killed | 183.0 |
| 186 | Sub-Saharan Africa | Republic of the Congo | 35 | 1 | Killed | 182.0 |
| 187 | Middle East & North Africa | Algeria | 182 | 0 | Wounded | 180.0 |
| 188 | Central Asia | Azerbaijan | 44 | 1 | Wounded | 180.0 |
| 189 | Middle East & North Africa | West Bank and Gaza Strip | 461 | 0 | Killed | 178.0 |
| 190 | Sub-Saharan Africa | Liberia | 32 | 1 | Killed | 177.0 |
| 191 | Sub-Saharan Africa | Ivory Coast | 67 | 1 | Wounded | 171.0 |
| 192 | Sub-Saharan Africa | Madagascar | 23 | 1 | Wounded | 169.0 |
| 193 | Southeast Asia | Thailand | 223 | 0 | Wounded | 164.0 |
| 194 | Sub-Saharan Africa | Djibouti | 22 | 1 | Wounded | 162.0 |
| 195 | South America | Bolivia | 273 | 1 | Wounded | 160.0 |
| 196 | Sub-Saharan Africa | Rhodesia | 80 | 1 | Wounded | 158.0 |
| 197 | South Asia | Sri Lanka | 173 | 0 | Killed | 153.0 |
| 198 | Sub-Saharan Africa | Zimbabwe | 96 | 1 | Killed | 152.0 |
| 199 | Southeast Asia | Malaysia | 86 | 1 | Killed | 152.0 |
| 200 | Eastern Europe | Russia | 384 | 0 | Wounded | 152.0 |
| 201 | South America | Peru | 341 | 0 | Wounded | 150.0 |
| 202 | Eastern Europe | Soviet Union | 67 | 1 | Wounded | 149.0 |
| 203 | South America | Brazil | 237 | 1 | Wounded | 148.0 |
| 204 | Eastern Europe | Bosnia-Herzegovina | 151 | 1 | Wounded | 148.0 |
| 205 | Middle East & North Africa | Libya | 263 | 0 | Wounded | 148.0 |
| 206 | Middle East & North Africa | Israel | 500 | 0 | Wounded | 141.0 |
| 207 | South America | Peru | 341 | 0 | Killed | 140.0 |
| 208 | North America | Canada | 75 | 1 | Wounded | 139.0 |
| 209 | Sub-Saharan Africa | Burkina Faso | 45 | 1 | Killed | 133.0 |
| 210 | Sub-Saharan Africa | Nigeria | 314 | 0 | Wounded | 133.0 |
| 211 | East Asia | South Korea | 34 | 1 | Wounded | 133.0 |
| 212 | Middle East & North Africa | Jordan | 84 | 1 | Killed | 131.0 |
| 213 | Middle East & North Africa | United Arab Emirates | 17 | 1 | Killed | 123.0 |
| 214 | Central America & Caribbean | Dominican Republic | 85 | 1 | Wounded | 123.0 |
| 215 | Western Europe | Austria | 88 | 1 | Wounded | 122.0 |
| 216 | Sub-Saharan Africa | Sierra Leone | 98 | 1 | Wounded | 122.0 |
| 217 | Sub-Saharan Africa | South Sudan | 32 | 0 | Killed | 119.0 |
| 218 | Sub-Saharan Africa | Niger | 8 | 0 | Killed | 118.0 |
| 219 | South Asia | Maldives | 18 | 1 | Wounded | 118.0 |
| 220 | Eastern Europe | Russia | 384 | 0 | Killed | 116.0 |
| 221 | Western Europe | Ireland | 139 | 1 | Killed | 115.0 |
| 222 | Eastern Europe | Albania | 64 | 1 | Wounded | 115.0 |
| 223 | Eastern Europe | Yugoslavia | 179 | 1 | Killed | 114.0 |
| 224 | Australasia & Oceania | Australia | 97 | 1 | Wounded | 112.0 |
| 225 | Middle East & North Africa | Syria | 82 | 0 | Killed | 110.0 |
| 226 | Western Europe | United Kingdom | 1029 | 0 | Killed | 110.0 |
| 227 | Southeast Asia | Malaysia | 86 | 1 | Wounded | 100.0 |
| 228 | Western Europe | West Germany (FRG) | 465 | 1 | Killed | 93.0 |
| 229 | Eastern Europe | Soviet Union | 67 | 1 | Killed | 93.0 |
| 230 | Middle East & North Africa | Libya | 263 | 0 | Killed | 91.0 |
| 231 | Eastern Europe | Moldova | 18 | 1 | Wounded | 88.0 |
| 232 | Western Europe | Norway | 16 | 1 | Wounded | 87.0 |
| 233 | Australasia & Oceania | Papua New Guinea | 77 | 1 | Wounded | 87.0 |
| 234 | Sub-Saharan Africa | South Africa | 139 | 0 | Wounded | 87.0 |
| 235 | Western Europe | Portugal | 129 | 1 | Wounded | 86.0 |
| 236 | Western Europe | Germany | 659 | 1 | Killed | 84.0 |
| 237 | Eastern Europe | Kosovo | 172 | 1 | Killed | 83.0 |
| 238 | Central America & Caribbean | Panama | 110 | 1 | Wounded | 82.0 |
| 239 | Sub-Saharan Africa | Sudan | 34 | 0 | Killed | 82.0 |
| 240 | Southeast Asia | South Vietnam | 1 | 1 | Killed | 81.0 |
| 241 | Western Europe | Switzerland | 90 | 1 | Wounded | 81.0 |
| 242 | Eastern Europe | Bosnia-Herzegovina | 151 | 1 | Killed | 79.0 |
| 243 | Western Europe | Norway | 16 | 1 | Killed | 79.0 |
| 244 | Western Europe | Belgium | 125 | 1 | Killed | 79.0 |
| 245 | East Asia | Taiwan | 37 | 1 | Wounded | 78.0 |
| 246 | Sub-Saharan Africa | Democratic Republic of the Congo | 58 | 0 | Killed | 78.0 |
| 247 | Western Europe | Sweden | 113 | 1 | Wounded | 77.0 |
| 248 | Central America & Caribbean | Barbados | 3 | 1 | Killed | 76.0 |
| 249 | East Asia | Hong Kong | 20 | 1 | Wounded | 75.0 |
| 250 | South America | Ecuador | 203 | 1 | Wounded | 74.0 |
| 251 | Australasia & Oceania | Papua New Guinea | 77 | 1 | Killed | 74.0 |
| 252 | Eastern Europe | Croatia | 55 | 1 | Wounded | 73.0 |
| 253 | Sub-Saharan Africa | Tanzania | 50 | 1 | Killed | 73.0 |
| 254 | Southeast Asia | Laos | 24 | 1 | Wounded | 73.0 |
| 255 | Western Europe | Switzerland | 90 | 1 | Killed | 73.0 |
| 256 | Eastern Europe | Ukraine | 180 | 0 | Wounded | 72.0 |
| 257 | Sub-Saharan Africa | Zambia | 58 | 1 | Killed | 70.0 |
| 258 | Central Asia | Armenia | 20 | 1 | Wounded | 70.0 |
| 259 | North America | United States | 496 | 0 | Wounded | 68.0 |
| 260 | Central Asia | Uzbekistan | 19 | 1 | Killed | 67.0 |
| 261 | East Asia | Japan | 341 | 1 | Killed | 66.0 |
| 262 | Middle East & North Africa | Lebanon | 296 | 0 | Killed | 65.0 |
| 263 | South America | Paraguay | 101 | 1 | Wounded | 63.0 |
| 264 | Middle East & North Africa | Syria | 82 | 0 | Wounded | 62.0 |
| 265 | Sub-Saharan Africa | Eritrea | 10 | 1 | Wounded | 62.0 |
| 266 | Middle East & North Africa | Kuwait | 63 | 1 | Killed | 61.0 |
| 267 | Sub-Saharan Africa | Zambia | 58 | 1 | Wounded | 61.0 |
| 268 | East Asia | Taiwan | 37 | 1 | Killed | 60.0 |
| 269 | Sub-Saharan Africa | Togo | 42 | 1 | Killed | 60.0 |
| 270 | South America | Paraguay | 101 | 1 | Killed | 59.0 |
| 271 | Middle East & North Africa | Algeria | 182 | 0 | Killed | 58.0 |
| 272 | South America | Chile | 144 | 0 | Wounded | 58.0 |
| 273 | Western Europe | France | 212 | 0 | Wounded | 57.0 |
| 274 | Western Europe | Italy | 173 | 0 | Wounded | 56.0 |
| 275 | Sub-Saharan Africa | Guinea | 21 | 1 | Wounded | 56.0 |
| 276 | Sub-Saharan Africa | Republic of the Congo | 35 | 1 | Wounded | 54.0 |
| 277 | Eastern Europe | Macedonia | 107 | 1 | Wounded | 53.0 |
| 278 | South America | Ecuador | 203 | 1 | Killed | 53.0 |
| 279 | Central America & Caribbean | Guatemala | 114 | 0 | Wounded | 53.0 |
| 280 | South Asia | Nepal | 259 | 0 | Wounded | 53.0 |
| 281 | Middle East & North Africa | Iran | 90 | 0 | Wounded | 53.0 |
| 282 | Sub-Saharan Africa | Cameroon | 23 | 0 | Killed | 49.0 |
| 283 | Central America & Caribbean | El Salvador | 93 | 0 | Killed | 49.0 |
| 284 | Central America & Caribbean | El Salvador | 93 | 0 | Wounded | 49.0 |
| 285 | Eastern Europe | Macedonia | 107 | 1 | Killed | 48.0 |
| 286 | Sub-Saharan Africa | Eritrea | 10 | 1 | Killed | 46.0 |
| 287 | Sub-Saharan Africa | Lesotho | 25 | 1 | Killed | 45.0 |
| 288 | Middle East & North Africa | Bahrain | 179 | 1 | Killed | 44.0 |
| 289 | East Asia | Macau | 27 | 1 | Wounded | 44.0 |
| 290 | Western Europe | Netherlands | 107 | 1 | Wounded | 44.0 |
| 291 | Central America & Caribbean | Guadeloupe | 47 | 1 | Wounded | 43.0 |
| 292 | Western Europe | Cyprus | 112 | 1 | Killed | 42.0 |
| 293 | Eastern Europe | Albania | 64 | 1 | Killed | 42.0 |
| 294 | Sub-Saharan Africa | Mauritania | 14 | 1 | Killed | 42.0 |
| 295 | Central America & Caribbean | Jamaica | 32 | 1 | Killed | 41.0 |
| 296 | Middle East & North Africa | Saudi Arabia | 52 | 0 | Killed | 41.0 |
| 297 | South America | Guyana | 19 | 1 | Killed | 40.0 |
| 298 | South America | Bolivia | 273 | 1 | Killed | 40.0 |
| 299 | Middle East & North Africa | Israel | 500 | 0 | Killed | 39.0 |
| 300 | South America | Argentina | 101 | 0 | Wounded | 38.0 |
| 301 | Sub-Saharan Africa | Angola | 13 | 0 | Killed | 38.0 |
| 302 | Central Asia | Kazakhstan | 22 | 1 | Killed | 37.0 |
| 303 | Central America & Caribbean | Panama | 110 | 1 | Killed | 37.0 |
| 304 | Central Asia | Armenia | 20 | 1 | Killed | 37.0 |
| 305 | Sub-Saharan Africa | Liberia | 32 | 1 | Wounded | 36.0 |
| 306 | Eastern Europe | Latvia | 13 | 1 | Wounded | 36.0 |
| 307 | Eastern Europe | East Germany (GDR) | 35 | 1 | Wounded | 36.0 |
| 308 | Western Europe | Cyprus | 112 | 1 | Wounded | 36.0 |
| 309 | Eastern Europe | Bulgaria | 46 | 1 | Wounded | 36.0 |
| 310 | Australasia & Oceania | New Caledonia | 28 | 1 | Killed | 35.0 |
| 311 | Middle East & North Africa | Saudi Arabia | 52 | 0 | Wounded | 35.0 |
| 312 | Central America & Caribbean | Costa Rica | 56 | 1 | Wounded | 34.0 |
| 313 | Western Europe | Spain | 431 | 0 | Killed | 34.0 |
| 314 | Central America & Caribbean | Dominican Republic | 85 | 1 | Killed | 34.0 |
| 315 | Central America & Caribbean | Trinidad and Tobago | 20 | 1 | Wounded | 34.0 |
| 316 | Central America & Caribbean | Guatemala | 114 | 0 | Killed | 34.0 |
| 317 | Sub-Saharan Africa | Uganda | 31 | 0 | Wounded | 34.0 |
| 318 | Sub-Saharan Africa | Malawi | 5 | 1 | Killed | 33.0 |
| 319 | Central America & Caribbean | Jamaica | 32 | 1 | Wounded | 33.0 |
| 320 | Sub-Saharan Africa | Togo | 42 | 1 | Wounded | 32.0 |
| 321 | Western Europe | Portugal | 129 | 1 | Killed | 31.0 |
| 322 | Eastern Europe | Poland | 34 | 1 | Wounded | 30.0 |
| 323 | Eastern Europe | Czech Republic | 23 | 1 | Wounded | 29.0 |
| 324 | Sub-Saharan Africa | Lesotho | 25 | 1 | Wounded | 29.0 |
| 325 | Western Europe | Ireland | 139 | 1 | Wounded | 29.0 |
| 326 | Central America & Caribbean | Nicaragua | 31 | 0 | Killed | 29.0 |
| 327 | Western Europe | Netherlands | 107 | 1 | Killed | 29.0 |
| 328 | Western Europe | Denmark | 35 | 1 | Wounded | 28.0 |
| 329 | Sub-Saharan Africa | Mauritania | 14 | 1 | Wounded | 28.0 |
| 330 | Western Europe | Austria | 88 | 1 | Killed | 28.0 |
| 331 | East Asia | Hong Kong | 6 | 0 | Wounded | 27.0 |
| 332 | Western Europe | Finland | 19 | 1 | Wounded | 27.0 |
| 333 | Southeast Asia | Vietnam | 7 | 1 | Wounded | 27.0 |
| 334 | Southeast Asia | Laos | 24 | 1 | Killed | 27.0 |
| 335 | Sub-Saharan Africa | Kenya | 75 | 0 | Killed | 27.0 |
| 336 | Sub-Saharan Africa | South Africa | 139 | 0 | Killed | 27.0 |
| 337 | Sub-Saharan Africa | Guinea-Bissau | 8 | 1 | Wounded | 27.0 |
| 338 | Eastern Europe | Czechoslovakia | 7 | 1 | Killed | 27.0 |
| 339 | Western Europe | Greece | 149 | 0 | Wounded | 26.0 |
| 340 | South America | Suriname | 61 | 1 | Killed | 26.0 |
| 341 | Middle East & North Africa | Iran | 90 | 0 | Killed | 26.0 |
| 342 | Eastern Europe | Bulgaria | 46 | 1 | Killed | 26.0 |
| 343 | Central Asia | Kyrgyzstan | 27 | 1 | Wounded | 25.0 |
| 344 | Middle East & North Africa | United Arab Emirates | 17 | 1 | Wounded | 25.0 |
| 345 | Central America & Caribbean | Nicaragua | 31 | 0 | Wounded | 25.0 |
| 346 | South America | Suriname | 61 | 1 | Wounded | 24.0 |
| 347 | Sub-Saharan Africa | Burundi | 23 | 0 | Killed | 24.0 |
| 348 | Sub-Saharan Africa | Madagascar | 23 | 1 | Killed | 23.0 |
| 349 | North America | Mexico | 45 | 0 | Wounded | 23.0 |
| 350 | Sub-Saharan Africa | Angola | 13 | 0 | Wounded | 23.0 |
| 351 | Southeast Asia | Thailand | 223 | 0 | Killed | 23.0 |
| 352 | Sub-Saharan Africa | Mozambique | 17 | 0 | Killed | 22.0 |
| 353 | Australasia & Oceania | Australia | 97 | 1 | Killed | 22.0 |
| 354 | Sub-Saharan Africa | Burundi | 23 | 0 | Wounded | 22.0 |
| 355 | South America | Guyana | 19 | 1 | Wounded | 22.0 |
| 356 | Sub-Saharan Africa | Sudan | 34 | 0 | Wounded | 22.0 |
| 357 | Eastern Europe | Czechoslovakia | 7 | 1 | Wounded | 21.0 |
| 358 | North America | Mexico | 45 | 0 | Killed | 21.0 |
| 359 | South America | Chile | 144 | 0 | Killed | 21.0 |
| 360 | Australasia & Oceania | New Caledonia | 28 | 1 | Wounded | 21.0 |
| 361 | Western Europe | Sweden | 113 | 1 | Killed | 21.0 |
| 362 | Western Europe | France | 212 | 0 | Killed | 21.0 |
| 363 | South Asia | Maldives | 18 | 1 | Killed | 20.0 |
| 364 | Central America & Caribbean | Grenada | 3 | 1 | Wounded | 20.0 |
| 365 | Sub-Saharan Africa | Mali | 43 | 0 | Killed | 20.0 |
| 366 | Southeast Asia | Myanmar | 37 | 0 | Killed | 20.0 |
| 367 | Central Asia | Georgia | 25 | 0 | Wounded | 20.0 |
| 368 | Central Asia | Kazakhstan | 22 | 1 | Wounded | 20.0 |
| 369 | Central America & Caribbean | Honduras | 37 | 0 | Wounded | 19.0 |
| 370 | Sub-Saharan Africa | Ghana | 15 | 1 | Killed | 19.0 |
| 371 | Australasia & Oceania | Fiji | 16 | 1 | Wounded | 18.0 |
| 372 | Central America & Caribbean | Haiti | 28 | 0 | Wounded | 18.0 |
| 373 | Western Europe | Germany | 76 | 0 | Wounded | 18.0 |
| 374 | Southeast Asia | Indonesia | 95 | 0 | Wounded | 17.0 |
| 375 | South Asia | Bangladesh | 129 | 0 | Killed | 17.0 |
| 376 | Sub-Saharan Africa | Guinea-Bissau | 8 | 1 | Killed | 17.0 |
| 377 | Sub-Saharan Africa | Chad | 4 | 0 | Killed | 17.0 |
| 378 | Sub-Saharan Africa | Ethiopia | 9 | 0 | Killed | 17.0 |
| 379 | East Asia | China | 32 | 0 | Wounded | 16.0 |
| 380 | Sub-Saharan Africa | Madagascar | 4 | 0 | Wounded | 16.0 |
| 381 | Sub-Saharan Africa | Kenya | 75 | 0 | Wounded | 16.0 |
| 382 | Eastern Europe | Hungary | 40 | 1 | Wounded | 16.0 |
| 383 | Sub-Saharan Africa | Togo | 6 | 0 | Killed | 16.0 |
| 384 | South America | Venezuela | 47 | 0 | Wounded | 15.0 |
| 385 | Sub-Saharan Africa | Ivory Coast | 7 | 0 | Killed | 15.0 |
| 386 | Western Europe | West Germany (FRG) | 76 | 0 | Wounded | 15.0 |
| 387 | Sub-Saharan Africa | People's Republic of the Congo | 3 | 1 | Killed | 15.0 |
| 388 | Eastern Europe | Belarus | 13 | 1 | Killed | 14.0 |
| 389 | Western Europe | Netherlands | 23 | 0 | Wounded | 14.0 |
| 390 | Middle East & North Africa | Tunisia | 12 | 0 | Killed | 14.0 |
| 391 | Middle East & North Africa | Qatar | 6 | 1 | Wounded | 13.0 |
| 392 | Australasia & Oceania | French Polynesia | 3 | 1 | Wounded | 13.0 |
| 393 | Sub-Saharan Africa | Gambia | 2 | 0 | Killed | 13.0 |
| 394 | Eastern Europe | Moldova | 18 | 1 | Killed | 13.0 |
| 395 | Western Europe | Switzerland | 21 | 0 | Wounded | 13.0 |
| 396 | North America | United States | 496 | 0 | Killed | 13.0 |
| 397 | Western Europe | Italy | 173 | 0 | Killed | 13.0 |
| 398 | South America | French Guiana | 6 | 1 | Wounded | 13.0 |
| 399 | Central America & Caribbean | Costa Rica | 56 | 1 | Killed | 12.0 |
| 400 | South America | Brazil | 36 | 0 | Wounded | 12.0 |
| 401 | Western Europe | Malta | 22 | 1 | Wounded | 12.0 |
| 402 | Middle East & North Africa | International | 1 | 1 | Wounded | 12.0 |
| 403 | Central America & Caribbean | St. Lucia | 1 | 1 | Wounded | 12.0 |
| 404 | Sub-Saharan Africa | South Sudan | 32 | 0 | Wounded | 12.0 |
| 405 | Sub-Saharan Africa | Botswana | 10 | 1 | Wounded | 11.0 |
| 406 | Sub-Saharan Africa | Botswana | 10 | 1 | Killed | 11.0 |
| 407 | Southeast Asia | Indonesia | 95 | 0 | Killed | 11.0 |
| 408 | Eastern Europe | Albania | 16 | 0 | Wounded | 11.0 |
| 409 | Western Europe | Finland | 19 | 1 | Killed | 11.0 |
| 410 | Sub-Saharan Africa | Central African Republic | 20 | 0 | Wounded | 11.0 |
| 411 | Eastern Europe | Slovak Republic | 15 | 1 | Wounded | 11.0 |
| 412 | Eastern Europe | Estonia | 16 | 1 | Wounded | 11.0 |
| 413 | Central Asia | Kyrgyzstan | 27 | 1 | Killed | 10.0 |
| 414 | Central America & Caribbean | Dominica | 2 | 1 | Wounded | 10.0 |
| 415 | East Asia | South Korea | 34 | 1 | Killed | 10.0 |
| 416 | Middle East & North Africa | Jordan | 29 | 0 | Wounded | 9.0 |
| 417 | Middle East & North Africa | Kuwait | 13 | 0 | Wounded | 9.0 |
| 418 | East Asia | Taiwan | 13 | 0 | Wounded | 9.0 |
| 419 | South Asia | Bhutan | 6 | 1 | Killed | 9.0 |
| 420 | Central America & Caribbean | St. Kitts and Nevis | 2 | 1 | Wounded | 9.0 |
| 421 | Eastern Europe | Serbia | 11 | 1 | Wounded | 9.0 |
| 422 | Sub-Saharan Africa | Ghana | 15 | 1 | Wounded | 9.0 |
| 423 | Eastern Europe | Poland | 34 | 1 | Killed | 9.0 |
| 424 | Western Europe | Portugal | 11 | 0 | Wounded | 9.0 |
| 425 | Central America & Caribbean | Guadeloupe | 47 | 1 | Killed | 8.0 |
| 426 | Australasia & Oceania | Fiji | 16 | 1 | Killed | 8.0 |
| 427 | Western Europe | Netherlands | 23 | 0 | Killed | 8.0 |
| 428 | Sub-Saharan Africa | Madagascar | 4 | 0 | Killed | 8.0 |
| 429 | Sub-Saharan Africa | Uganda | 31 | 0 | Killed | 8.0 |
| 430 | Central America & Caribbean | Grenada | 3 | 1 | Killed | 8.0 |
| 431 | Sub-Saharan Africa | Benin | 8 | 1 | Wounded | 8.0 |
| 432 | Eastern Europe | Kosovo | 24 | 0 | Wounded | 8.0 |
| 433 | East Asia | Japan | 61 | 0 | Wounded | 8.0 |
| 434 | Middle East & North Africa | Qatar | 6 | 1 | Killed | 7.0 |
| 435 | Southeast Asia | East Timor | 7 | 1 | Killed | 7.0 |
| 436 | North America | Canada | 21 | 0 | Wounded | 7.0 |
| 437 | Sub-Saharan Africa | Central African Republic | 20 | 0 | Killed | 7.0 |
| 438 | Eastern Europe | Yugoslavia | 24 | 0 | Wounded | 7.0 |
| 439 | Southeast Asia | Myanmar | 37 | 0 | Wounded | 7.0 |
| 440 | Central Asia | Azerbaijan | 5 | 0 | Wounded | 7.0 |
| 441 | Central America & Caribbean | Cuba | 21 | 1 | Wounded | 7.0 |
| 442 | Sub-Saharan Africa | Sierra Leone | 3 | 0 | Killed | 7.0 |
| 443 | Eastern Europe | Macedonia | 11 | 0 | Wounded | 7.0 |
| 444 | South America | Bolivia | 41 | 0 | Wounded | 6.0 |
| 445 | Sub-Saharan Africa | Swaziland | 15 | 1 | Killed | 6.0 |
| 446 | Central Asia | Tajikistan | 8 | 0 | Wounded | 6.0 |
| 447 | South America | Argentina | 101 | 0 | Killed | 6.0 |
| 448 | South America | Uruguay | 71 | 1 | Wounded | 6.0 |
| 449 | Eastern Europe | Ukraine | 180 | 0 | Killed | 6.0 |
| 450 | South America | Uruguay | 71 | 1 | Killed | 6.0 |
| 451 | South America | Paraguay | 13 | 0 | Wounded | 6.0 |
| 452 | Central Asia | Georgia | 25 | 0 | Killed | 6.0 |
| 453 | Sub-Saharan Africa | Gabon | 6 | 1 | Killed | 6.0 |
| 454 | Western Europe | Luxembourg | 14 | 1 | Wounded | 6.0 |
| 455 | Eastern Europe | Czech Republic | 23 | 1 | Killed | 6.0 |
| 456 | Eastern Europe | Slovak Republic | 15 | 1 | Killed | 6.0 |
| 457 | Southeast Asia | East Timor | 7 | 1 | Wounded | 6.0 |
| 458 | Eastern Europe | Hungary | 40 | 1 | Killed | 6.0 |
| 459 | Eastern Europe | Romania | 4 | 1 | Wounded | 6.0 |
| 460 | Central America & Caribbean | Trinidad and Tobago | 20 | 1 | Killed | 6.0 |
| 461 | East Asia | China | 32 | 0 | Killed | 6.0 |
| 462 | Sub-Saharan Africa | Niger | 8 | 0 | Wounded | 5.0 |
| 463 | Sub-Saharan Africa | Mali | 43 | 0 | Wounded | 5.0 |
| 464 | South America | Ecuador | 17 | 0 | Wounded | 5.0 |
| 465 | Australasia & Oceania | Papua New Guinea | 12 | 0 | Killed | 5.0 |
| 466 | South America | Venezuela | 47 | 0 | Killed | 5.0 |
| 467 | Eastern Europe | Yugoslavia | 24 | 0 | Killed | 5.0 |
| 468 | Southeast Asia | Singapore | 7 | 1 | Killed | 5.0 |
| 469 | Eastern Europe | Serbia-Montenegro | 10 | 1 | Wounded | 5.0 |
| 470 | Western Europe | Denmark | 35 | 1 | Killed | 5.0 |
| 471 | Central America & Caribbean | Costa Rica | 11 | 0 | Killed | 5.0 |
| 472 | Western Europe | Malta | 22 | 1 | Killed | 5.0 |
| 473 | South Asia | Bhutan | 6 | 1 | Wounded | 5.0 |
| 474 | Western Europe | Cyprus | 20 | 0 | Wounded | 5.0 |
| 475 | Sub-Saharan Africa | Lesotho | 4 | 0 | Wounded | 5.0 |
| 476 | Western Europe | West Germany (FRG) | 76 | 0 | Killed | 4.0 |
| 477 | South Asia | Maldives | 4 | 0 | Wounded | 4.0 |
| 478 | Western Europe | Greece | 149 | 0 | Killed | 4.0 |
| 479 | Western Europe | Belgium | 29 | 0 | Wounded | 4.0 |
| 480 | Southeast Asia | Cambodia | 16 | 0 | Wounded | 4.0 |
| 481 | South Asia | Nepal | 259 | 0 | Killed | 4.0 |
| 482 | Western Europe | Austria | 27 | 0 | Wounded | 4.0 |
| 483 | Sub-Saharan Africa | Ghana | 4 | 0 | Wounded | 4.0 |
| 484 | Australasia & Oceania | Papua New Guinea | 12 | 0 | Wounded | 4.0 |
| 485 | Central America & Caribbean | Costa Rica | 11 | 0 | Wounded | 4.0 |
| 486 | East Asia | North Korea | 1 | 1 | Wounded | 4.0 |
| 487 | Middle East & North Africa | Western Sahara | 5 | 1 | Wounded | 4.0 |
| 488 | Australasia & Oceania | Solomon Islands | 4 | 1 | Killed | 4.0 |
| 489 | Central America & Caribbean | Cuba | 9 | 0 | Killed | 4.0 |
| 490 | Central America & Caribbean | Cuba | 21 | 1 | Killed | 4.0 |
| 491 | Sub-Saharan Africa | Mozambique | 17 | 0 | Wounded | 4.0 |
| 492 | Sub-Saharan Africa | Democratic Republic of the Congo | 58 | 0 | Wounded | 4.0 |
| 493 | Sub-Saharan Africa | Equatorial Guinea | 2 | 1 | Wounded | 3.0 |
| 494 | Western Europe | Vatican City | 1 | 0 | Wounded | 3.0 |
| 495 | Sub-Saharan Africa | Gabon | 2 | 0 | Wounded | 3.0 |
| 496 | Central Asia | Turkmenistan | 1 | 1 | Killed | 3.0 |
| 497 | Eastern Europe | Estonia | 16 | 1 | Killed | 3.0 |
| 498 | Eastern Europe | Serbia-Montenegro | 10 | 1 | Killed | 3.0 |
| 499 | Southeast Asia | Singapore | 7 | 1 | Wounded | 3.0 |
| 500 | Western Europe | Cyprus | 20 | 0 | Killed | 3.0 |
| 501 | Sub-Saharan Africa | Swaziland | 15 | 1 | Wounded | 3.0 |
| 502 | Central America & Caribbean | Cuba | 9 | 0 | Wounded | 3.0 |
| 503 | Central America & Caribbean | Dominica | 2 | 1 | Killed | 3.0 |
| 504 | Central America & Caribbean | Barbados | 3 | 1 | Wounded | 3.0 |
| 505 | Western Europe | Sweden | 19 | 0 | Wounded | 3.0 |
| 506 | Eastern Europe | Romania | 2 | 0 | Wounded | 3.0 |
| 507 | South America | Suriname | 5 | 0 | Killed | 3.0 |
| 508 | Central America & Caribbean | Jamaica | 4 | 0 | Wounded | 3.0 |
| 509 | Eastern Europe | Romania | 4 | 1 | Killed | 3.0 |
| 510 | Eastern Europe | Serbia | 11 | 1 | Killed | 3.0 |
| 511 | East Asia | North Korea | 1 | 1 | Killed | 3.0 |
| 512 | Central America & Caribbean | Belize | 7 | 1 | Killed | 3.0 |
| 513 | Central America & Caribbean | Honduras | 37 | 0 | Killed | 3.0 |
| 514 | Eastern Europe | Soviet Union | 11 | 0 | Killed | 3.0 |
| 515 | East Asia | Macau | 6 | 0 | Wounded | 2.0 |
| 516 | Sub-Saharan Africa | Togo | 6 | 0 | Wounded | 2.0 |
| 517 | Sub-Saharan Africa | Tanzania | 9 | 0 | Wounded | 2.0 |
| 518 | South America | Guyana | 7 | 0 | Wounded | 2.0 |
| 519 | Sub-Saharan Africa | Gambia | 1 | 1 | Wounded | 2.0 |
| 520 | Western Europe | Ireland | 168 | 0 | Wounded | 2.0 |
| 521 | Eastern Europe | Slovenia | 6 | 1 | Wounded | 2.0 |
| 522 | South America | Brazil | 36 | 0 | Killed | 2.0 |
| 523 | South America | Bolivia | 41 | 0 | Killed | 2.0 |
| 524 | Central America & Caribbean | St. Lucia | 1 | 1 | Killed | 2.0 |
| 525 | Central Asia | Kazakhstan | 5 | 0 | Killed | 2.0 |
| 526 | Middle East & North Africa | United Arab Emirates | 5 | 0 | Wounded | 2.0 |
| 527 | Central America & Caribbean | Guadeloupe | 9 | 0 | Wounded | 2.0 |
| 528 | Middle East & North Africa | Tunisia | 12 | 0 | Wounded | 2.0 |
| 529 | Middle East & North Africa | Jordan | 29 | 0 | Killed | 2.0 |
| 530 | Middle East & North Africa | Kuwait | 13 | 0 | Killed | 2.0 |
| 531 | Middle East & North Africa | South Yemen | 2 | 1 | Wounded | 2.0 |
| 532 | Sub-Saharan Africa | Zimbabwe | 5 | 0 | Killed | 2.0 |
| 533 | Central Asia | Tajikistan | 8 | 0 | Killed | 2.0 |
| 534 | Eastern Europe | East Germany (GDR) | 35 | 1 | Killed | 2.0 |
| 535 | Central Asia | Turkmenistan | 1 | 1 | Wounded | 2.0 |
| 536 | East Asia | Hong Kong | 20 | 1 | Killed | 2.0 |
| 537 | Australasia & Oceania | New Zealand | 11 | 1 | Wounded | 2.0 |
| 538 | East Asia | Hong Kong | 6 | 0 | Killed | 2.0 |
| 539 | Western Europe | Ireland | 168 | 0 | Killed | 2.0 |
| 540 | Eastern Europe | Bosnia-Herzegovina | 8 | 0 | Wounded | 2.0 |
| 541 | Australasia & Oceania | New Caledonia | 3 | 0 | Wounded | 2.0 |
| 542 | Eastern Europe | Bulgaria | 6 | 0 | Killed | 2.0 |
| 543 | Southeast Asia | East Timor | 3 | 0 | Killed | 2.0 |
| 544 | Sub-Saharan Africa | Comoros | 3 | 1 | Wounded | 2.0 |
| 545 | Eastern Europe | Czechoslovakia | 3 | 0 | Wounded | 2.0 |
| 546 | Middle East & North Africa | North Yemen | 4 | 1 | Killed | 2.0 |
| 547 | Sub-Saharan Africa | Equatorial Guinea | 2 | 1 | Killed | 2.0 |
| 548 | Western Europe | Austria | 27 | 0 | Killed | 2.0 |
| 549 | Sub-Saharan Africa | Ethiopia | 9 | 0 | Wounded | 2.0 |
| 550 | Eastern Europe | Latvia | 13 | 1 | Killed | 2.0 |
| 551 | Sub-Saharan Africa | Zimbabwe | 5 | 0 | Wounded | 2.0 |
| 552 | Western Europe | Portugal | 11 | 0 | Killed | 1.0 |
| 553 | Sub-Saharan Africa | Rwanda | 5 | 0 | Wounded | 1.0 |
| 554 | Sub-Saharan Africa | Rwanda | 5 | 0 | Killed | 1.0 |
| 555 | Western Europe | Norway | 3 | 0 | Wounded | 1.0 |
| 556 | Western Europe | Sweden | 19 | 0 | Killed | 1.0 |
| 557 | Western Europe | Denmark | 6 | 0 | Wounded | 1.0 |
| 558 | Sub-Saharan Africa | Namibia | 4 | 0 | Wounded | 1.0 |
| 559 | Western Europe | Switzerland | 21 | 0 | Killed | 1.0 |
| 560 | Sub-Saharan Africa | Senegal | 2 | 0 | Wounded | 1.0 |
| 561 | Sub-Saharan Africa | Zambia | 4 | 0 | Wounded | 1.0 |
| 562 | Sub-Saharan Africa | Mauritania | 4 | 0 | Killed | 1.0 |
| 563 | Australasia & Oceania | Australia | 17 | 0 | Killed | 1.0 |
| 564 | South America | Ecuador | 17 | 0 | Killed | 1.0 |
| 565 | Eastern Europe | Slovenia | 6 | 1 | Killed | 1.0 |
| 566 | Eastern Europe | Romania | 2 | 0 | Killed | 1.0 |
| 567 | Southeast Asia | Cambodia | 16 | 0 | Killed | 1.0 |
| 568 | Southeast Asia | Brunei | 6 | 0 | Wounded | 1.0 |
| 569 | Central America & Caribbean | Dominican Republic | 5 | 0 | Wounded | 1.0 |
| 570 | Central America & Caribbean | Grenada | 2 | 0 | Killed | 1.0 |
| 571 | South Asia | Mauritius | 2 | 0 | Wounded | 1.0 |
| 572 | Central America & Caribbean | Grenada | 2 | 0 | Wounded | 1.0 |
| 573 | Central Asia | Uzbekistan | 2 | 0 | Killed | 1.0 |
| 574 | Central America & Caribbean | Haiti | 28 | 0 | Killed | 1.0 |
| 575 | Eastern Europe | Slovak Republic | 3 | 0 | Killed | 1.0 |
| 576 | Central America & Caribbean | Jamaica | 4 | 0 | Killed | 1.0 |
| 577 | Central Asia | Turkmenistan | 1 | 0 | Wounded | 1.0 |
| 578 | South America | Guyana | 7 | 0 | Killed | 1.0 |
| 579 | Central Asia | Kyrgyzstan | 8 | 0 | Wounded | 1.0 |
| 580 | Southeast Asia | East Timor | 3 | 0 | Wounded | 1.0 |
| 581 | Eastern Europe | Soviet Union | 11 | 0 | Wounded | 1.0 |
| 582 | South America | French Guiana | 6 | 1 | Killed | 1.0 |
| 583 | Central America & Caribbean | Martinique | 12 | 1 | Wounded | 1.0 |
| 584 | Central America & Caribbean | Panama | 17 | 0 | Killed | 1.0 |
| 585 | Central America & Caribbean | Panama | 17 | 0 | Wounded | 1.0 |
| 586 | Central Asia | Kazakhstan | 5 | 0 | Wounded | 1.0 |
| 587 | Middle East & North Africa | Western Sahara | 5 | 1 | Killed | 1.0 |
| 588 | Central America & Caribbean | Trinidad and Tobago | 2 | 0 | Wounded | 1.0 |
| 589 | Middle East & North Africa | Bahrain | 28 | 0 | Wounded | 1.0 |
| 590 | Central Asia | Armenia | 4 | 0 | Wounded | 1.0 |
| 591 | Middle East & North Africa | International | 1 | 1 | Killed | 1.0 |
| 592 | Middle East & North Africa | North Yemen | 4 | 1 | Wounded | 1.0 |
| 593 | Central Asia | Azerbaijan | 5 | 0 | Killed | 1.0 |
| 594 | Middle East & North Africa | Morocco | 3 | 0 | Wounded | 1.0 |
| 595 | Eastern Europe | Poland | 5 | 0 | Wounded | 1.0 |
| 596 | Middle East & North Africa | North Yemen | 2 | 0 | Killed | 1.0 |
| 597 | East Asia | South Korea | 4 | 0 | Wounded | 1.0 |
| 598 | Australasia & Oceania | New Zealand | 11 | 1 | Killed | 1.0 |
| 599 | Eastern Europe | Lithuania | 7 | 1 | Killed | 1.0 |
| 600 | Eastern Europe | Lithuania | 1 | 0 | Wounded | 1.0 |
| 601 | Sub-Saharan Africa | Comoros | 2 | 0 | Killed | 1.0 |
| 602 | East Asia | Macau | 27 | 1 | Killed | 1.0 |
| 603 | Eastern Europe | Lithuania | 7 | 1 | Wounded | 1.0 |
| 604 | Eastern Europe | Macedonia | 11 | 0 | Killed | 1.0 |
| 605 | Sub-Saharan Africa | Burkina Faso | 7 | 0 | Wounded | 1.0 |
| 606 | Sub-Saharan Africa | Burkina Faso | 7 | 0 | Killed | 1.0 |
| 607 | Eastern Europe | Hungary | 6 | 0 | Wounded | 1.0 |
| 608 | Southeast Asia | Vietnam | 7 | 1 | Killed | 1.0 |
| 609 | Sub-Saharan Africa | Guinea | 4 | 0 | Wounded | 1.0 |
| 610 | Central America & Caribbean | Bahamas | 4 | 1 | Killed | 1.0 |
| 611 | Eastern Europe | East Germany (GDR) | 3 | 0 | Wounded | 1.0 |
| 612 | Southeast Asia | Malaysia | 13 | 0 | Wounded | 1.0 |
| 613 | Australasia & Oceania | Australia | 17 | 0 | Wounded | 1.0 |
| 614 | Sub-Saharan Africa | Lesotho | 4 | 0 | Killed | 1.0 |
| 615 | Eastern Europe | Montenegro | 5 | 1 | Killed | 1.0 |
| 616 | Eastern Europe | Bulgaria | 6 | 0 | Wounded | 0.0 |
| 617 | Eastern Europe | Croatia | 2 | 0 | Wounded | 0.0 |
| 618 | Eastern Europe | Czech Republic | 9 | 0 | Killed | 0.0 |
| 619 | Eastern Europe | Croatia | 2 | 0 | Killed | 0.0 |
| 620 | Central Asia | Kyrgyzstan | 8 | 0 | Killed | 0.0 |
| 621 | East Asia | Macau | 6 | 0 | Killed | 0.0 |
| 622 | Western Europe | Denmark | 6 | 0 | Killed | 0.0 |
| 623 | Eastern Europe | Bosnia-Herzegovina | 8 | 0 | Killed | 0.0 |
| 624 | Eastern Europe | Albania | 16 | 0 | Killed | 0.0 |
| 625 | East Asia | Taiwan | 13 | 0 | Killed | 0.0 |
| 626 | Western Europe | Finland | 1 | 0 | Killed | 0.0 |
| 627 | Western Europe | Finland | 1 | 0 | Wounded | 0.0 |
| 628 | Western Europe | Germany | 76 | 0 | Killed | 0.0 |
| 629 | Central Asia | Uzbekistan | 2 | 0 | Wounded | 0.0 |
| 630 | East Asia | Japan | 61 | 0 | Killed | 0.0 |
| 631 | East Asia | South Korea | 4 | 0 | Killed | 0.0 |
| 632 | Central Asia | Turkmenistan | 1 | 0 | Killed | 0.0 |
| 633 | Western Europe | Luxembourg | 2 | 0 | Killed | 0.0 |
| 634 | Western Europe | Iceland | 4 | 1 | Killed | 0.0 |
| 635 | Australasia & Oceania | New Zealand | 9 | 0 | Wounded | 0.0 |
| 636 | Central America & Caribbean | Antigua and Barbuda | 1 | 1 | Wounded | 0.0 |
| 637 | Central America & Caribbean | Antigua and Barbuda | 1 | 1 | Killed | 0.0 |
| 638 | Australasia & Oceania | Wallis and Futuna | 1 | 1 | Wounded | 0.0 |
| 639 | Australasia & Oceania | Wallis and Futuna | 1 | 1 | Killed | 0.0 |
| 640 | Australasia & Oceania | Vanuatu | 2 | 1 | Wounded | 0.0 |
| 641 | Australasia & Oceania | Vanuatu | 2 | 1 | Killed | 0.0 |
| 642 | Australasia & Oceania | Solomon Islands | 4 | 1 | Wounded | 0.0 |
| 643 | Australasia & Oceania | New Zealand | 9 | 0 | Killed | 0.0 |
| 644 | Central America & Caribbean | Bahamas | 1 | 0 | Wounded | 0.0 |
| 645 | Australasia & Oceania | New Hebrides | 1 | 1 | Wounded | 0.0 |
| 646 | Australasia & Oceania | New Hebrides | 1 | 1 | Killed | 0.0 |
| 647 | Australasia & Oceania | New Caledonia | 3 | 0 | Killed | 0.0 |
| 648 | Western Europe | Vatican City | 1 | 0 | Killed | 0.0 |
| 649 | Australasia & Oceania | French Polynesia | 3 | 1 | Killed | 0.0 |
| 650 | Australasia & Oceania | Fiji | 1 | 0 | Wounded | 0.0 |
| 651 | Australasia & Oceania | Fiji | 1 | 0 | Killed | 0.0 |
| 652 | Central America & Caribbean | Bahamas | 1 | 0 | Killed | 0.0 |
| 653 | Central America & Caribbean | Bahamas | 4 | 1 | Wounded | 0.0 |
| 654 | Western Europe | Iceland | 4 | 1 | Wounded | 0.0 |
| 655 | Western Europe | Malta | 1 | 0 | Killed | 0.0 |
| 656 | Central Asia | Armenia | 4 | 0 | Killed | 0.0 |
| 657 | Central America & Caribbean | Trinidad and Tobago | 2 | 0 | Killed | 0.0 |
| 658 | Central America & Caribbean | St. Kitts and Nevis | 2 | 1 | Killed | 0.0 |
| 659 | Eastern Europe | Czechoslovakia | 3 | 0 | Killed | 0.0 |
| 660 | Western Europe | Luxembourg | 14 | 1 | Killed | 0.0 |
| 661 | Western Europe | Luxembourg | 2 | 0 | Wounded | 0.0 |
| 662 | Central America & Caribbean | Martinique | 12 | 1 | Killed | 0.0 |
| 663 | Western Europe | Malta | 1 | 0 | Wounded | 0.0 |
| 664 | Central America & Caribbean | Belize | 1 | 0 | Killed | 0.0 |
| 665 | Central America & Caribbean | Guadeloupe | 9 | 0 | Killed | 0.0 |
| 666 | Western Europe | Norway | 3 | 0 | Killed | 0.0 |
| 667 | Central America & Caribbean | Dominican Republic | 5 | 0 | Killed | 0.0 |
| 668 | Central America & Caribbean | Dominica | 1 | 0 | Wounded | 0.0 |
| 669 | Central America & Caribbean | Dominica | 1 | 0 | Killed | 0.0 |
| 670 | Central America & Caribbean | Belize | 7 | 1 | Wounded | 0.0 |
| 671 | Central America & Caribbean | Belize | 1 | 0 | Wounded | 0.0 |
| 672 | Eastern Europe | Czech Republic | 9 | 0 | Wounded | 0.0 |
| 673 | Sub-Saharan Africa | Liberia | 2 | 0 | Wounded | 0.0 |
| 674 | Eastern Europe | East Germany (GDR) | 3 | 0 | Killed | 0.0 |
| 675 | Sub-Saharan Africa | Republic of the Congo | 1 | 0 | Wounded | 0.0 |
| 676 | Southeast Asia | Malaysia | 13 | 0 | Killed | 0.0 |
| 677 | Southeast Asia | Laos | 3 | 0 | Wounded | 0.0 |
| 678 | Southeast Asia | Laos | 3 | 0 | Killed | 0.0 |
| 679 | Sub-Saharan Africa | People's Republic of the Congo | 1 | 0 | Killed | 0.0 |
| 680 | Sub-Saharan Africa | People's Republic of the Congo | 1 | 0 | Wounded | 0.0 |
| 681 | Sub-Saharan Africa | People's Republic of the Congo | 3 | 1 | Wounded | 0.0 |
| 682 | Sub-Saharan Africa | Republic of the Congo | 1 | 0 | Killed | 0.0 |
| 683 | Sub-Saharan Africa | Rhodesia | 3 | 0 | Killed | 0.0 |
| 684 | Southeast Asia | Vietnam | 5 | 0 | Killed | 0.0 |
| 685 | Southeast Asia | Brunei | 6 | 0 | Killed | 0.0 |
| 686 | Sub-Saharan Africa | Rhodesia | 3 | 0 | Wounded | 0.0 |
| 687 | South Asia | Mauritius | 2 | 0 | Killed | 0.0 |
| 688 | South Asia | Maldives | 4 | 0 | Killed | 0.0 |
| 689 | South America | Uruguay | 11 | 0 | Wounded | 0.0 |
| 690 | South America | Uruguay | 11 | 0 | Killed | 0.0 |
| 691 | Sub-Saharan Africa | Senegal | 2 | 0 | Killed | 0.0 |
| 692 | Southeast Asia | South Vietnam | 1 | 1 | Wounded | 0.0 |
| 693 | Southeast Asia | Vietnam | 5 | 0 | Wounded | 0.0 |
| 694 | Western Europe | Belgium | 29 | 0 | Killed | 0.0 |
| 695 | Sub-Saharan Africa | Gambia | 1 | 1 | Killed | 0.0 |
| 696 | Sub-Saharan Africa | Ivory Coast | 7 | 0 | Wounded | 0.0 |
| 697 | Sub-Saharan Africa | Guinea-Bissau | 1 | 0 | Wounded | 0.0 |
| 698 | Sub-Saharan Africa | Guinea-Bissau | 1 | 0 | Killed | 0.0 |
| 699 | Sub-Saharan Africa | Malawi | 5 | 1 | Wounded | 0.0 |
| 700 | Sub-Saharan Africa | Guinea | 4 | 0 | Killed | 0.0 |
| 701 | Sub-Saharan Africa | Ghana | 4 | 0 | Killed | 0.0 |
| 702 | Sub-Saharan Africa | Gambia | 2 | 0 | Wounded | 0.0 |
| 703 | Sub-Saharan Africa | Gabon | 6 | 1 | Wounded | 0.0 |
| 704 | Sub-Saharan Africa | Benin | 8 | 1 | Killed | 0.0 |
| 705 | Sub-Saharan Africa | Gabon | 2 | 0 | Killed | 0.0 |
| 706 | Sub-Saharan Africa | Mauritania | 4 | 0 | Wounded | 0.0 |
| 707 | Sub-Saharan Africa | Comoros | 2 | 0 | Wounded | 0.0 |
| 708 | Sub-Saharan Africa | Comoros | 3 | 1 | Killed | 0.0 |
| 709 | Sub-Saharan Africa | Chad | 4 | 0 | Wounded | 0.0 |
| 710 | Sub-Saharan Africa | Cameroon | 23 | 0 | Wounded | 0.0 |
| 711 | Sub-Saharan Africa | Namibia | 4 | 0 | Killed | 0.0 |
| 712 | South America | Suriname | 5 | 0 | Wounded | 0.0 |
| 713 | South America | Paraguay | 13 | 0 | Killed | 0.0 |
| 714 | Sub-Saharan Africa | Seychelles | 2 | 1 | Killed | 0.0 |
| 715 | Eastern Europe | Moldova | 3 | 0 | Killed | 0.0 |
| 716 | Eastern Europe | Serbia | 1 | 0 | Wounded | 0.0 |
| 717 | Eastern Europe | Serbia | 1 | 0 | Killed | 0.0 |
| 718 | Eastern Europe | Poland | 5 | 0 | Killed | 0.0 |
| 719 | Eastern Europe | Montenegro | 5 | 1 | Wounded | 0.0 |
| 720 | Sub-Saharan Africa | Zaire | 5 | 0 | Killed | 0.0 |
| 721 | Eastern Europe | Moldova | 3 | 0 | Wounded | 0.0 |
| 722 | Sub-Saharan Africa | Zaire | 5 | 0 | Wounded | 0.0 |
| 723 | Sub-Saharan Africa | Liberia | 2 | 0 | Killed | 0.0 |
| 724 | Sub-Saharan Africa | Seychelles | 2 | 1 | Wounded | 0.0 |
| 725 | Eastern Europe | Lithuania | 1 | 0 | Killed | 0.0 |
| 726 | Eastern Europe | Latvia | 4 | 0 | Wounded | 0.0 |
| 727 | Eastern Europe | Latvia | 4 | 0 | Killed | 0.0 |
| 728 | Western Europe | Andorra | 1 | 1 | Killed | 0.0 |
| 729 | Western Europe | Andorra | 1 | 1 | Wounded | 0.0 |
| 730 | Eastern Europe | Kosovo | 24 | 0 | Killed | 0.0 |
| 731 | Eastern Europe | Hungary | 6 | 0 | Killed | 0.0 |
| 732 | Eastern Europe | Serbia-Montenegro | 1 | 0 | Killed | 0.0 |
| 733 | Eastern Europe | Serbia-Montenegro | 1 | 0 | Wounded | 0.0 |
| 734 | Eastern Europe | Slovak Republic | 3 | 0 | Wounded | 0.0 |
| 735 | Sub-Saharan Africa | Tanzania | 9 | 0 | Killed | 0.0 |
| 736 | South America | French Guiana | 1 | 0 | Wounded | 0.0 |
| 737 | Sub-Saharan Africa | Sierra Leone | 3 | 0 | Wounded | 0.0 |
| 738 | South America | French Guiana | 1 | 0 | Killed | 0.0 |
| 739 | South America | Falkland Islands | 1 | 1 | Wounded | 0.0 |
| 740 | South America | Falkland Islands | 1 | 1 | Killed | 0.0 |
| 741 | North America | Canada | 21 | 0 | Killed | 0.0 |
| 742 | Middle East & North Africa | United Arab Emirates | 5 | 0 | Killed | 0.0 |
| 743 | Middle East & North Africa | South Yemen | 2 | 1 | Killed | 0.0 |
| 744 | Middle East & North Africa | Qatar | 1 | 0 | Wounded | 0.0 |
| 745 | Middle East & North Africa | Qatar | 1 | 0 | Killed | 0.0 |
| 746 | Middle East & North Africa | North Yemen | 2 | 0 | Wounded | 0.0 |
| 747 | Middle East & North Africa | Morocco | 3 | 0 | Killed | 0.0 |
| 748 | Middle East & North Africa | Bahrain | 28 | 0 | Killed | 0.0 |
| 749 | Sub-Saharan Africa | Swaziland | 1 | 0 | Killed | 0.0 |
| 750 | Sub-Saharan Africa | Swaziland | 1 | 0 | Wounded | 0.0 |
| 751 | Sub-Saharan Africa | Zambia | 4 | 0 | Killed | 0.0 |
# Group by Country and Year and sum the number of killings
country_killings = df_terr.groupby(['Country','success'])[['Killed','Wounded']].sum().reset_index()
# Sort the data by the number of killings in descending order
# top_countries=country_killings.nlargest(10,'Killed')
# top_countries
country_killings
| Country | success | Killed | Wounded | |
|---|---|---|---|---|
| 0 | Afghanistan | 0 | 2832.0 | 2634.0 |
| 1 | Afghanistan | 1 | 36552.0 | 41643.0 |
| 2 | Albania | 0 | 0.0 | 11.0 |
| 3 | Albania | 1 | 42.0 | 115.0 |
| 4 | Algeria | 0 | 58.0 | 180.0 |
| 5 | Algeria | 1 | 11008.0 | 8970.0 |
| 6 | Andorra | 1 | 0.0 | 0.0 |
| 7 | Angola | 0 | 38.0 | 23.0 |
| 8 | Angola | 1 | 3005.0 | 2432.0 |
| 9 | Antigua and Barbuda | 1 | 0.0 | 0.0 |
| 10 | Argentina | 0 | 6.0 | 38.0 |
| 11 | Argentina | 1 | 484.0 | 717.0 |
| 12 | Armenia | 0 | 0.0 | 1.0 |
| 13 | Armenia | 1 | 37.0 | 70.0 |
| 14 | Australia | 0 | 1.0 | 1.0 |
| 15 | Australia | 1 | 22.0 | 112.0 |
| 16 | Austria | 0 | 2.0 | 4.0 |
| 17 | Austria | 1 | 28.0 | 122.0 |
| 18 | Azerbaijan | 0 | 1.0 | 7.0 |
| 19 | Azerbaijan | 1 | 257.0 | 180.0 |
| 20 | Bahamas | 0 | 0.0 | 0.0 |
| 21 | Bahamas | 1 | 1.0 | 0.0 |
| 22 | Bahrain | 0 | 0.0 | 1.0 |
| 23 | Bahrain | 1 | 44.0 | 188.0 |
| 24 | Bangladesh | 0 | 17.0 | 233.0 |
| 25 | Bangladesh | 1 | 1227.0 | 7992.0 |
| 26 | Barbados | 1 | 76.0 | 3.0 |
| 27 | Belarus | 1 | 14.0 | 212.0 |
| 28 | Belgium | 0 | 0.0 | 4.0 |
| 29 | Belgium | 1 | 79.0 | 512.0 |
| 30 | Belize | 0 | 0.0 | 0.0 |
| 31 | Belize | 1 | 3.0 | 0.0 |
| 32 | Benin | 1 | 0.0 | 8.0 |
| 33 | Bhutan | 1 | 9.0 | 5.0 |
| 34 | Bolivia | 0 | 2.0 | 6.0 |
| 35 | Bolivia | 1 | 40.0 | 160.0 |
| 36 | Bosnia-Herzegovina | 0 | 0.0 | 2.0 |
| 37 | Bosnia-Herzegovina | 1 | 79.0 | 148.0 |
| 38 | Botswana | 1 | 11.0 | 11.0 |
| 39 | Brazil | 0 | 2.0 | 12.0 |
| 40 | Brazil | 1 | 201.0 | 148.0 |
| 41 | Brunei | 0 | 0.0 | 1.0 |
| 42 | Bulgaria | 0 | 2.0 | 0.0 |
| 43 | Bulgaria | 1 | 26.0 | 36.0 |
| 44 | Burkina Faso | 0 | 1.0 | 1.0 |
| 45 | Burkina Faso | 1 | 133.0 | 218.0 |
| 46 | Burundi | 0 | 24.0 | 22.0 |
| 47 | Burundi | 1 | 4181.0 | 2432.0 |
| 48 | Cambodia | 0 | 1.0 | 4.0 |
| 49 | Cambodia | 1 | 542.0 | 782.0 |
| 50 | Cameroon | 0 | 49.0 | 0.0 |
| 51 | Cameroon | 1 | 2298.0 | 1063.0 |
| 52 | Canada | 0 | 0.0 | 7.0 |
| 53 | Canada | 1 | 365.0 | 139.0 |
| 54 | Central African Republic | 0 | 7.0 | 11.0 |
| 55 | Central African Republic | 1 | 1983.0 | 931.0 |
| 56 | Chad | 0 | 17.0 | 0.0 |
| 57 | Chad | 1 | 1102.0 | 1681.0 |
| 58 | Chile | 0 | 21.0 | 58.0 |
| 59 | Chile | 1 | 207.0 | 697.0 |
| 60 | China | 0 | 6.0 | 16.0 |
| 61 | China | 1 | 1002.0 | 1826.0 |
| 62 | Colombia | 0 | 317.0 | 311.0 |
| 63 | Colombia | 1 | 14381.0 | 10017.0 |
| 64 | Comoros | 0 | 1.0 | 0.0 |
| 65 | Comoros | 1 | 0.0 | 2.0 |
| 66 | Costa Rica | 0 | 5.0 | 4.0 |
| 67 | Costa Rica | 1 | 12.0 | 34.0 |
| 68 | Croatia | 0 | 0.0 | 0.0 |
| 69 | Croatia | 1 | 248.0 | 73.0 |
| 70 | Cuba | 0 | 4.0 | 3.0 |
| 71 | Cuba | 1 | 4.0 | 7.0 |
| 72 | Cyprus | 0 | 3.0 | 5.0 |
| 73 | Cyprus | 1 | 42.0 | 36.0 |
| 74 | Czech Republic | 0 | 0.0 | 0.0 |
| 75 | Czech Republic | 1 | 6.0 | 29.0 |
| 76 | Czechoslovakia | 0 | 0.0 | 2.0 |
| 77 | Czechoslovakia | 1 | 27.0 | 21.0 |
| 78 | Democratic Republic of the Congo | 0 | 78.0 | 4.0 |
| 79 | Democratic Republic of the Congo | 1 | 3991.0 | 1365.0 |
| 80 | Denmark | 0 | 0.0 | 1.0 |
| 81 | Denmark | 1 | 5.0 | 28.0 |
| 82 | Djibouti | 1 | 274.0 | 162.0 |
| 83 | Dominica | 0 | 0.0 | 0.0 |
| 84 | Dominica | 1 | 3.0 | 10.0 |
| 85 | Dominican Republic | 0 | 0.0 | 1.0 |
| 86 | Dominican Republic | 1 | 34.0 | 123.0 |
| 87 | East Germany (GDR) | 0 | 0.0 | 1.0 |
| 88 | East Germany (GDR) | 1 | 2.0 | 36.0 |
| 89 | East Timor | 0 | 2.0 | 1.0 |
| 90 | East Timor | 1 | 7.0 | 6.0 |
| 91 | Ecuador | 0 | 1.0 | 5.0 |
| 92 | Ecuador | 1 | 53.0 | 74.0 |
| 93 | Egypt | 0 | 281.0 | 183.0 |
| 94 | Egypt | 1 | 3588.0 | 4639.0 |
| 95 | El Salvador | 0 | 49.0 | 49.0 |
| 96 | El Salvador | 1 | 12004.0 | 5013.0 |
| 97 | Equatorial Guinea | 1 | 2.0 | 3.0 |
| 98 | Eritrea | 1 | 46.0 | 62.0 |
| 99 | Estonia | 1 | 3.0 | 11.0 |
| 100 | Ethiopia | 0 | 17.0 | 2.0 |
| 101 | Ethiopia | 1 | 1748.0 | 1212.0 |
| 102 | Falkland Islands | 1 | 0.0 | 0.0 |
| 103 | Fiji | 0 | 0.0 | 0.0 |
| 104 | Fiji | 1 | 8.0 | 18.0 |
| 105 | Finland | 0 | 0.0 | 0.0 |
| 106 | Finland | 1 | 11.0 | 27.0 |
| 107 | France | 0 | 21.0 | 57.0 |
| 108 | France | 1 | 513.0 | 2459.0 |
| 109 | French Guiana | 0 | 0.0 | 0.0 |
| 110 | French Guiana | 1 | 1.0 | 13.0 |
| 111 | French Polynesia | 1 | 0.0 | 13.0 |
| 112 | Gabon | 0 | 0.0 | 3.0 |
| 113 | Gabon | 1 | 6.0 | 0.0 |
| 114 | Gambia | 0 | 13.0 | 0.0 |
| 115 | Gambia | 1 | 0.0 | 2.0 |
| 116 | Georgia | 0 | 6.0 | 20.0 |
| 117 | Georgia | 1 | 272.0 | 372.0 |
| 118 | Germany | 0 | 0.0 | 18.0 |
| 119 | Germany | 1 | 84.0 | 665.0 |
| 120 | Ghana | 0 | 0.0 | 4.0 |
| 121 | Ghana | 1 | 19.0 | 9.0 |
| 122 | Greece | 0 | 4.0 | 26.0 |
| 123 | Greece | 1 | 321.0 | 707.0 |
| 124 | Grenada | 0 | 1.0 | 1.0 |
| 125 | Grenada | 1 | 8.0 | 20.0 |
| 126 | Guadeloupe | 0 | 0.0 | 2.0 |
| 127 | Guadeloupe | 1 | 8.0 | 43.0 |
| 128 | Guatemala | 0 | 34.0 | 53.0 |
| 129 | Guatemala | 1 | 5133.0 | 1178.0 |
| 130 | Guinea | 0 | 0.0 | 1.0 |
| 131 | Guinea | 1 | 213.0 | 56.0 |
| 132 | Guinea-Bissau | 0 | 0.0 | 0.0 |
| 133 | Guinea-Bissau | 1 | 17.0 | 27.0 |
| 134 | Guyana | 0 | 1.0 | 2.0 |
| 135 | Guyana | 1 | 40.0 | 22.0 |
| 136 | Haiti | 0 | 1.0 | 18.0 |
| 137 | Haiti | 1 | 335.0 | 282.0 |
| 138 | Honduras | 0 | 3.0 | 19.0 |
| 139 | Honduras | 1 | 304.0 | 221.0 |
| 140 | Hong Kong | 0 | 2.0 | 27.0 |
| 141 | Hong Kong | 1 | 2.0 | 75.0 |
| 142 | Hungary | 0 | 0.0 | 1.0 |
| 143 | Hungary | 1 | 6.0 | 16.0 |
| 144 | Iceland | 1 | 0.0 | 0.0 |
| 145 | India | 0 | 222.0 | 607.0 |
| 146 | India | 1 | 19119.0 | 28373.0 |
| 147 | Indonesia | 0 | 11.0 | 17.0 |
| 148 | Indonesia | 1 | 1227.0 | 2428.0 |
| 149 | International | 1 | 1.0 | 12.0 |
| 150 | Iran | 0 | 26.0 | 53.0 |
| 151 | Iran | 1 | 1647.0 | 3976.0 |
| 152 | Iraq | 0 | 5553.0 | 2118.0 |
| 153 | Iraq | 1 | 73036.0 | 132572.0 |
| 154 | Ireland | 0 | 2.0 | 2.0 |
| 155 | Ireland | 1 | 115.0 | 29.0 |
| 156 | Israel | 0 | 39.0 | 141.0 |
| 157 | Israel | 1 | 1664.0 | 7805.0 |
| 158 | Italy | 0 | 13.0 | 56.0 |
| 159 | Italy | 1 | 407.0 | 1235.0 |
| 160 | Ivory Coast | 0 | 15.0 | 0.0 |
| 161 | Ivory Coast | 1 | 253.0 | 171.0 |
| 162 | Jamaica | 0 | 1.0 | 3.0 |
| 163 | Jamaica | 1 | 41.0 | 33.0 |
| 164 | Japan | 0 | 0.0 | 8.0 |
| 165 | Japan | 1 | 66.0 | 6990.0 |
| 166 | Jordan | 0 | 2.0 | 9.0 |
| 167 | Jordan | 1 | 131.0 | 251.0 |
| 168 | Kazakhstan | 0 | 2.0 | 1.0 |
| 169 | Kazakhstan | 1 | 37.0 | 20.0 |
| 170 | Kenya | 0 | 27.0 | 16.0 |
| 171 | Kenya | 1 | 1921.0 | 6247.0 |
| 172 | Kosovo | 0 | 0.0 | 8.0 |
| 173 | Kosovo | 1 | 83.0 | 357.0 |
| 174 | Kuwait | 0 | 2.0 | 9.0 |
| 175 | Kuwait | 1 | 61.0 | 291.0 |
| 176 | Kyrgyzstan | 0 | 0.0 | 1.0 |
| 177 | Kyrgyzstan | 1 | 10.0 | 25.0 |
| 178 | Laos | 0 | 0.0 | 0.0 |
| 179 | Laos | 1 | 27.0 | 73.0 |
| 180 | Latvia | 0 | 0.0 | 0.0 |
| 181 | Latvia | 1 | 2.0 | 36.0 |
| 182 | Lebanon | 0 | 65.0 | 251.0 |
| 183 | Lebanon | 1 | 3996.0 | 10653.0 |
| 184 | Lesotho | 0 | 1.0 | 5.0 |
| 185 | Lesotho | 1 | 45.0 | 29.0 |
| 186 | Liberia | 0 | 0.0 | 0.0 |
| 187 | Liberia | 1 | 177.0 | 36.0 |
| 188 | Libya | 0 | 91.0 | 148.0 |
| 189 | Libya | 1 | 2507.0 | 3162.0 |
| 190 | Lithuania | 0 | 0.0 | 1.0 |
| 191 | Lithuania | 1 | 1.0 | 1.0 |
| 192 | Luxembourg | 0 | 0.0 | 0.0 |
| 193 | Luxembourg | 1 | 0.0 | 6.0 |
| 194 | Macau | 0 | 0.0 | 2.0 |
| 195 | Macau | 1 | 1.0 | 44.0 |
| 196 | Macedonia | 0 | 1.0 | 7.0 |
| 197 | Macedonia | 1 | 48.0 | 53.0 |
| 198 | Madagascar | 0 | 8.0 | 16.0 |
| 199 | Madagascar | 1 | 23.0 | 169.0 |
| 200 | Malawi | 1 | 33.0 | 0.0 |
| 201 | Malaysia | 0 | 0.0 | 1.0 |
| 202 | Malaysia | 1 | 152.0 | 100.0 |
| 203 | Maldives | 0 | 0.0 | 4.0 |
| 204 | Maldives | 1 | 20.0 | 118.0 |
| 205 | Mali | 0 | 20.0 | 5.0 |
| 206 | Mali | 1 | 1412.0 | 1351.0 |
| 207 | Malta | 0 | 0.0 | 0.0 |
| 208 | Malta | 1 | 5.0 | 12.0 |
| 209 | Martinique | 1 | 0.0 | 1.0 |
| 210 | Mauritania | 0 | 1.0 | 0.0 |
| 211 | Mauritania | 1 | 42.0 | 28.0 |
| 212 | Mauritius | 0 | 0.0 | 1.0 |
| 213 | Mexico | 0 | 21.0 | 23.0 |
| 214 | Mexico | 1 | 759.0 | 660.0 |
| 215 | Moldova | 0 | 0.0 | 0.0 |
| 216 | Moldova | 1 | 13.0 | 88.0 |
| 217 | Montenegro | 1 | 1.0 | 0.0 |
| 218 | Morocco | 0 | 0.0 | 1.0 |
| 219 | Morocco | 1 | 292.0 | 199.0 |
| 220 | Mozambique | 0 | 22.0 | 4.0 |
| 221 | Mozambique | 1 | 2689.0 | 1514.0 |
| 222 | Myanmar | 0 | 20.0 | 7.0 |
| 223 | Myanmar | 1 | 1260.0 | 1624.0 |
| 224 | Namibia | 0 | 0.0 | 1.0 |
| 225 | Namibia | 1 | 220.0 | 405.0 |
| 226 | Nepal | 0 | 4.0 | 53.0 |
| 227 | Nepal | 1 | 1965.0 | 2098.0 |
| 228 | Netherlands | 0 | 8.0 | 14.0 |
| 229 | Netherlands | 1 | 29.0 | 44.0 |
| 230 | New Caledonia | 0 | 0.0 | 2.0 |
| 231 | New Caledonia | 1 | 35.0 | 21.0 |
| 232 | New Hebrides | 1 | 0.0 | 0.0 |
| 233 | New Zealand | 0 | 0.0 | 0.0 |
| 234 | New Zealand | 1 | 1.0 | 2.0 |
| 235 | Nicaragua | 0 | 29.0 | 25.0 |
| 236 | Nicaragua | 1 | 10569.0 | 1706.0 |
| 237 | Niger | 0 | 118.0 | 5.0 |
| 238 | Niger | 1 | 1356.0 | 467.0 |
| 239 | Nigeria | 0 | 454.0 | 133.0 |
| 240 | Nigeria | 1 | 22228.0 | 10106.0 |
| 241 | North Korea | 1 | 3.0 | 4.0 |
| 242 | North Yemen | 0 | 1.0 | 0.0 |
| 243 | North Yemen | 1 | 2.0 | 1.0 |
| 244 | Norway | 0 | 0.0 | 1.0 |
| 245 | Norway | 1 | 79.0 | 87.0 |
| 246 | Pakistan | 0 | 528.0 | 906.0 |
| 247 | Pakistan | 1 | 23294.0 | 41132.0 |
| 248 | Panama | 0 | 1.0 | 1.0 |
| 249 | Panama | 1 | 37.0 | 82.0 |
| 250 | Papua New Guinea | 0 | 5.0 | 4.0 |
| 251 | Papua New Guinea | 1 | 74.0 | 87.0 |
| 252 | Paraguay | 0 | 0.0 | 6.0 |
| 253 | Paraguay | 1 | 59.0 | 63.0 |
| 254 | People's Republic of the Congo | 0 | 0.0 | 0.0 |
| 255 | People's Republic of the Congo | 1 | 15.0 | 0.0 |
| 256 | Peru | 0 | 140.0 | 150.0 |
| 257 | Peru | 1 | 12631.0 | 3928.0 |
| 258 | Philippines | 0 | 270.0 | 512.0 |
| 259 | Philippines | 1 | 9289.0 | 12855.0 |
| 260 | Poland | 0 | 0.0 | 1.0 |
| 261 | Poland | 1 | 9.0 | 30.0 |
| 262 | Portugal | 0 | 1.0 | 9.0 |
| 263 | Portugal | 1 | 31.0 | 86.0 |
| 264 | Qatar | 0 | 0.0 | 0.0 |
| 265 | Qatar | 1 | 7.0 | 13.0 |
| 266 | Republic of the Congo | 0 | 0.0 | 0.0 |
| 267 | Republic of the Congo | 1 | 182.0 | 54.0 |
| 268 | Rhodesia | 0 | 0.0 | 0.0 |
| 269 | Rhodesia | 1 | 217.0 | 158.0 |
| 270 | Romania | 0 | 1.0 | 3.0 |
| 271 | Romania | 1 | 3.0 | 6.0 |
| 272 | Russia | 0 | 116.0 | 152.0 |
| 273 | Russia | 1 | 4192.0 | 7289.0 |
| 274 | Rwanda | 0 | 1.0 | 1.0 |
| 275 | Rwanda | 1 | 3235.0 | 921.0 |
| 276 | Saudi Arabia | 0 | 41.0 | 35.0 |
| 277 | Saudi Arabia | 1 | 631.0 | 1631.0 |
| 278 | Senegal | 0 | 0.0 | 1.0 |
| 279 | Senegal | 1 | 325.0 | 322.0 |
| 280 | Serbia | 0 | 0.0 | 0.0 |
| 281 | Serbia | 1 | 3.0 | 9.0 |
| 282 | Serbia-Montenegro | 0 | 0.0 | 0.0 |
| 283 | Serbia-Montenegro | 1 | 3.0 | 5.0 |
| 284 | Seychelles | 1 | 0.0 | 0.0 |
| 285 | Sierra Leone | 0 | 7.0 | 0.0 |
| 286 | Sierra Leone | 1 | 833.0 | 122.0 |
| 287 | Singapore | 1 | 5.0 | 3.0 |
| 288 | Slovak Republic | 0 | 1.0 | 0.0 |
| 289 | Slovak Republic | 1 | 6.0 | 11.0 |
| 290 | Slovenia | 1 | 1.0 | 2.0 |
| 291 | Solomon Islands | 1 | 4.0 | 0.0 |
| 292 | Somalia | 0 | 449.0 | 364.0 |
| 293 | Somalia | 1 | 9824.0 | 8511.0 |
| 294 | South Africa | 0 | 27.0 | 87.0 |
| 295 | South Africa | 1 | 2647.0 | 4458.0 |
| 296 | South Korea | 0 | 0.0 | 1.0 |
| 297 | South Korea | 1 | 10.0 | 133.0 |
| 298 | South Sudan | 0 | 119.0 | 12.0 |
| 299 | South Sudan | 1 | 2515.0 | 1311.0 |
| 300 | South Vietnam | 1 | 81.0 | 0.0 |
| 301 | South Yemen | 1 | 0.0 | 2.0 |
| 302 | Soviet Union | 0 | 3.0 | 1.0 |
| 303 | Soviet Union | 1 | 93.0 | 149.0 |
| 304 | Spain | 0 | 34.0 | 261.0 |
| 305 | Spain | 1 | 1254.0 | 4674.0 |
| 306 | Sri Lanka | 0 | 153.0 | 368.0 |
| 307 | Sri Lanka | 1 | 15377.0 | 15193.0 |
| 308 | St. Kitts and Nevis | 1 | 0.0 | 9.0 |
| 309 | St. Lucia | 1 | 2.0 | 12.0 |
| 310 | Sudan | 0 | 82.0 | 22.0 |
| 311 | Sudan | 1 | 3801.0 | 2147.0 |
| 312 | Suriname | 0 | 3.0 | 0.0 |
| 313 | Suriname | 1 | 26.0 | 24.0 |
| 314 | Swaziland | 0 | 0.0 | 0.0 |
| 315 | Swaziland | 1 | 6.0 | 3.0 |
| 316 | Sweden | 0 | 1.0 | 3.0 |
| 317 | Sweden | 1 | 21.0 | 77.0 |
| 318 | Switzerland | 0 | 1.0 | 13.0 |
| 319 | Switzerland | 1 | 73.0 | 81.0 |
| 320 | Syria | 0 | 110.0 | 62.0 |
| 321 | Syria | 1 | 15119.0 | 14047.0 |
| 322 | Taiwan | 0 | 0.0 | 9.0 |
| 323 | Taiwan | 1 | 60.0 | 78.0 |
| 324 | Tajikistan | 0 | 2.0 | 6.0 |
| 325 | Tajikistan | 1 | 305.0 | 1103.0 |
| 326 | Tanzania | 0 | 0.0 | 2.0 |
| 327 | Tanzania | 1 | 73.0 | 232.0 |
| 328 | Thailand | 0 | 23.0 | 164.0 |
| 329 | Thailand | 1 | 2719.0 | 7654.0 |
| 330 | Togo | 0 | 16.0 | 2.0 |
| 331 | Togo | 1 | 60.0 | 32.0 |
| 332 | Trinidad and Tobago | 0 | 0.0 | 1.0 |
| 333 | Trinidad and Tobago | 1 | 6.0 | 34.0 |
| 334 | Tunisia | 0 | 14.0 | 2.0 |
| 335 | Tunisia | 1 | 337.0 | 431.0 |
| 336 | Turkey | 0 | 183.0 | 197.0 |
| 337 | Turkey | 1 | 6705.0 | 9702.0 |
| 338 | Turkmenistan | 0 | 0.0 | 1.0 |
| 339 | Turkmenistan | 1 | 3.0 | 2.0 |
| 340 | Uganda | 0 | 8.0 | 34.0 |
| 341 | Uganda | 1 | 3057.0 | 1111.0 |
| 342 | Ukraine | 0 | 6.0 | 72.0 |
| 343 | Ukraine | 1 | 2255.0 | 2769.0 |
| 344 | United Arab Emirates | 0 | 0.0 | 2.0 |
| 345 | United Arab Emirates | 1 | 123.0 | 25.0 |
| 346 | United Kingdom | 0 | 110.0 | 243.0 |
| 347 | United Kingdom | 1 | 3300.0 | 5863.0 |
| 348 | United States | 0 | 13.0 | 68.0 |
| 349 | United States | 1 | 3758.0 | 20634.0 |
| 350 | Uruguay | 0 | 0.0 | 0.0 |
| 351 | Uruguay | 1 | 6.0 | 6.0 |
| 352 | Uzbekistan | 0 | 1.0 | 0.0 |
| 353 | Uzbekistan | 1 | 67.0 | 200.0 |
| 354 | Vanuatu | 1 | 0.0 | 0.0 |
| 355 | Vatican City | 0 | 0.0 | 3.0 |
| 356 | Venezuela | 0 | 5.0 | 15.0 |
| 357 | Venezuela | 1 | 222.0 | 232.0 |
| 358 | Vietnam | 0 | 0.0 | 0.0 |
| 359 | Vietnam | 1 | 1.0 | 27.0 |
| 360 | Wallis and Futuna | 1 | 0.0 | 0.0 |
| 361 | West Bank and Gaza Strip | 0 | 178.0 | 206.0 |
| 362 | West Bank and Gaza Strip | 1 | 1322.0 | 2808.0 |
| 363 | West Germany (FRG) | 0 | 4.0 | 15.0 |
| 364 | West Germany (FRG) | 1 | 93.0 | 847.0 |
| 365 | Western Sahara | 1 | 1.0 | 4.0 |
| 366 | Yemen | 0 | 377.0 | 341.0 |
| 367 | Yemen | 1 | 8399.0 | 8987.0 |
| 368 | Yugoslavia | 0 | 5.0 | 7.0 |
| 369 | Yugoslavia | 1 | 114.0 | 274.0 |
| 370 | Zaire | 0 | 0.0 | 0.0 |
| 371 | Zaire | 1 | 324.0 | 211.0 |
| 372 | Zambia | 0 | 0.0 | 1.0 |
| 373 | Zambia | 1 | 70.0 | 61.0 |
| 374 | Zimbabwe | 0 | 2.0 | 2.0 |
| 375 | Zimbabwe | 1 | 152.0 | 220.0 |
# Group by Country and Year and sum the number of killings
country_killings = df_terr.groupby(['Country','success'])['Killed'].sum().reset_index()
# Sort the data by the number of killings in descending order
# top_countries=country_killings.nlargest(10,'Killed')
# top_countries
country_killings
| Country | success | Killed | |
|---|---|---|---|
| 0 | Afghanistan | 0 | 2832.0 |
| 1 | Afghanistan | 1 | 36552.0 |
| 2 | Albania | 0 | 0.0 |
| 3 | Albania | 1 | 42.0 |
| 4 | Algeria | 0 | 58.0 |
| 5 | Algeria | 1 | 11008.0 |
| 6 | Andorra | 1 | 0.0 |
| 7 | Angola | 0 | 38.0 |
| 8 | Angola | 1 | 3005.0 |
| 9 | Antigua and Barbuda | 1 | 0.0 |
| 10 | Argentina | 0 | 6.0 |
| 11 | Argentina | 1 | 484.0 |
| 12 | Armenia | 0 | 0.0 |
| 13 | Armenia | 1 | 37.0 |
| 14 | Australia | 0 | 1.0 |
| 15 | Australia | 1 | 22.0 |
| 16 | Austria | 0 | 2.0 |
| 17 | Austria | 1 | 28.0 |
| 18 | Azerbaijan | 0 | 1.0 |
| 19 | Azerbaijan | 1 | 257.0 |
| 20 | Bahamas | 0 | 0.0 |
| 21 | Bahamas | 1 | 1.0 |
| 22 | Bahrain | 0 | 0.0 |
| 23 | Bahrain | 1 | 44.0 |
| 24 | Bangladesh | 0 | 17.0 |
| 25 | Bangladesh | 1 | 1227.0 |
| 26 | Barbados | 1 | 76.0 |
| 27 | Belarus | 1 | 14.0 |
| 28 | Belgium | 0 | 0.0 |
| 29 | Belgium | 1 | 79.0 |
| 30 | Belize | 0 | 0.0 |
| 31 | Belize | 1 | 3.0 |
| 32 | Benin | 1 | 0.0 |
| 33 | Bhutan | 1 | 9.0 |
| 34 | Bolivia | 0 | 2.0 |
| 35 | Bolivia | 1 | 40.0 |
| 36 | Bosnia-Herzegovina | 0 | 0.0 |
| 37 | Bosnia-Herzegovina | 1 | 79.0 |
| 38 | Botswana | 1 | 11.0 |
| 39 | Brazil | 0 | 2.0 |
| 40 | Brazil | 1 | 201.0 |
| 41 | Brunei | 0 | 0.0 |
| 42 | Bulgaria | 0 | 2.0 |
| 43 | Bulgaria | 1 | 26.0 |
| 44 | Burkina Faso | 0 | 1.0 |
| 45 | Burkina Faso | 1 | 133.0 |
| 46 | Burundi | 0 | 24.0 |
| 47 | Burundi | 1 | 4181.0 |
| 48 | Cambodia | 0 | 1.0 |
| 49 | Cambodia | 1 | 542.0 |
| 50 | Cameroon | 0 | 49.0 |
| 51 | Cameroon | 1 | 2298.0 |
| 52 | Canada | 0 | 0.0 |
| 53 | Canada | 1 | 365.0 |
| 54 | Central African Republic | 0 | 7.0 |
| 55 | Central African Republic | 1 | 1983.0 |
| 56 | Chad | 0 | 17.0 |
| 57 | Chad | 1 | 1102.0 |
| 58 | Chile | 0 | 21.0 |
| 59 | Chile | 1 | 207.0 |
| 60 | China | 0 | 6.0 |
| 61 | China | 1 | 1002.0 |
| 62 | Colombia | 0 | 317.0 |
| 63 | Colombia | 1 | 14381.0 |
| 64 | Comoros | 0 | 1.0 |
| 65 | Comoros | 1 | 0.0 |
| 66 | Costa Rica | 0 | 5.0 |
| 67 | Costa Rica | 1 | 12.0 |
| 68 | Croatia | 0 | 0.0 |
| 69 | Croatia | 1 | 248.0 |
| 70 | Cuba | 0 | 4.0 |
| 71 | Cuba | 1 | 4.0 |
| 72 | Cyprus | 0 | 3.0 |
| 73 | Cyprus | 1 | 42.0 |
| 74 | Czech Republic | 0 | 0.0 |
| 75 | Czech Republic | 1 | 6.0 |
| 76 | Czechoslovakia | 0 | 0.0 |
| 77 | Czechoslovakia | 1 | 27.0 |
| 78 | Democratic Republic of the Congo | 0 | 78.0 |
| 79 | Democratic Republic of the Congo | 1 | 3991.0 |
| 80 | Denmark | 0 | 0.0 |
| 81 | Denmark | 1 | 5.0 |
| 82 | Djibouti | 1 | 274.0 |
| 83 | Dominica | 0 | 0.0 |
| 84 | Dominica | 1 | 3.0 |
| 85 | Dominican Republic | 0 | 0.0 |
| 86 | Dominican Republic | 1 | 34.0 |
| 87 | East Germany (GDR) | 0 | 0.0 |
| 88 | East Germany (GDR) | 1 | 2.0 |
| 89 | East Timor | 0 | 2.0 |
| 90 | East Timor | 1 | 7.0 |
| 91 | Ecuador | 0 | 1.0 |
| 92 | Ecuador | 1 | 53.0 |
| 93 | Egypt | 0 | 281.0 |
| 94 | Egypt | 1 | 3588.0 |
| 95 | El Salvador | 0 | 49.0 |
| 96 | El Salvador | 1 | 12004.0 |
| 97 | Equatorial Guinea | 1 | 2.0 |
| 98 | Eritrea | 1 | 46.0 |
| 99 | Estonia | 1 | 3.0 |
| 100 | Ethiopia | 0 | 17.0 |
| 101 | Ethiopia | 1 | 1748.0 |
| 102 | Falkland Islands | 1 | 0.0 |
| 103 | Fiji | 0 | 0.0 |
| 104 | Fiji | 1 | 8.0 |
| 105 | Finland | 0 | 0.0 |
| 106 | Finland | 1 | 11.0 |
| 107 | France | 0 | 21.0 |
| 108 | France | 1 | 513.0 |
| 109 | French Guiana | 0 | 0.0 |
| 110 | French Guiana | 1 | 1.0 |
| 111 | French Polynesia | 1 | 0.0 |
| 112 | Gabon | 0 | 0.0 |
| 113 | Gabon | 1 | 6.0 |
| 114 | Gambia | 0 | 13.0 |
| 115 | Gambia | 1 | 0.0 |
| 116 | Georgia | 0 | 6.0 |
| 117 | Georgia | 1 | 272.0 |
| 118 | Germany | 0 | 0.0 |
| 119 | Germany | 1 | 84.0 |
| 120 | Ghana | 0 | 0.0 |
| 121 | Ghana | 1 | 19.0 |
| 122 | Greece | 0 | 4.0 |
| 123 | Greece | 1 | 321.0 |
| 124 | Grenada | 0 | 1.0 |
| 125 | Grenada | 1 | 8.0 |
| 126 | Guadeloupe | 0 | 0.0 |
| 127 | Guadeloupe | 1 | 8.0 |
| 128 | Guatemala | 0 | 34.0 |
| 129 | Guatemala | 1 | 5133.0 |
| 130 | Guinea | 0 | 0.0 |
| 131 | Guinea | 1 | 213.0 |
| 132 | Guinea-Bissau | 0 | 0.0 |
| 133 | Guinea-Bissau | 1 | 17.0 |
| 134 | Guyana | 0 | 1.0 |
| 135 | Guyana | 1 | 40.0 |
| 136 | Haiti | 0 | 1.0 |
| 137 | Haiti | 1 | 335.0 |
| 138 | Honduras | 0 | 3.0 |
| 139 | Honduras | 1 | 304.0 |
| 140 | Hong Kong | 0 | 2.0 |
| 141 | Hong Kong | 1 | 2.0 |
| 142 | Hungary | 0 | 0.0 |
| 143 | Hungary | 1 | 6.0 |
| 144 | Iceland | 1 | 0.0 |
| 145 | India | 0 | 222.0 |
| 146 | India | 1 | 19119.0 |
| 147 | Indonesia | 0 | 11.0 |
| 148 | Indonesia | 1 | 1227.0 |
| 149 | International | 1 | 1.0 |
| 150 | Iran | 0 | 26.0 |
| 151 | Iran | 1 | 1647.0 |
| 152 | Iraq | 0 | 5553.0 |
| 153 | Iraq | 1 | 73036.0 |
| 154 | Ireland | 0 | 2.0 |
| 155 | Ireland | 1 | 115.0 |
| 156 | Israel | 0 | 39.0 |
| 157 | Israel | 1 | 1664.0 |
| 158 | Italy | 0 | 13.0 |
| 159 | Italy | 1 | 407.0 |
| 160 | Ivory Coast | 0 | 15.0 |
| 161 | Ivory Coast | 1 | 253.0 |
| 162 | Jamaica | 0 | 1.0 |
| 163 | Jamaica | 1 | 41.0 |
| 164 | Japan | 0 | 0.0 |
| 165 | Japan | 1 | 66.0 |
| 166 | Jordan | 0 | 2.0 |
| 167 | Jordan | 1 | 131.0 |
| 168 | Kazakhstan | 0 | 2.0 |
| 169 | Kazakhstan | 1 | 37.0 |
| 170 | Kenya | 0 | 27.0 |
| 171 | Kenya | 1 | 1921.0 |
| 172 | Kosovo | 0 | 0.0 |
| 173 | Kosovo | 1 | 83.0 |
| 174 | Kuwait | 0 | 2.0 |
| 175 | Kuwait | 1 | 61.0 |
| 176 | Kyrgyzstan | 0 | 0.0 |
| 177 | Kyrgyzstan | 1 | 10.0 |
| 178 | Laos | 0 | 0.0 |
| 179 | Laos | 1 | 27.0 |
| 180 | Latvia | 0 | 0.0 |
| 181 | Latvia | 1 | 2.0 |
| 182 | Lebanon | 0 | 65.0 |
| 183 | Lebanon | 1 | 3996.0 |
| 184 | Lesotho | 0 | 1.0 |
| 185 | Lesotho | 1 | 45.0 |
| 186 | Liberia | 0 | 0.0 |
| 187 | Liberia | 1 | 177.0 |
| 188 | Libya | 0 | 91.0 |
| 189 | Libya | 1 | 2507.0 |
| 190 | Lithuania | 0 | 0.0 |
| 191 | Lithuania | 1 | 1.0 |
| 192 | Luxembourg | 0 | 0.0 |
| 193 | Luxembourg | 1 | 0.0 |
| 194 | Macau | 0 | 0.0 |
| 195 | Macau | 1 | 1.0 |
| 196 | Macedonia | 0 | 1.0 |
| 197 | Macedonia | 1 | 48.0 |
| 198 | Madagascar | 0 | 8.0 |
| 199 | Madagascar | 1 | 23.0 |
| 200 | Malawi | 1 | 33.0 |
| 201 | Malaysia | 0 | 0.0 |
| 202 | Malaysia | 1 | 152.0 |
| 203 | Maldives | 0 | 0.0 |
| 204 | Maldives | 1 | 20.0 |
| 205 | Mali | 0 | 20.0 |
| 206 | Mali | 1 | 1412.0 |
| 207 | Malta | 0 | 0.0 |
| 208 | Malta | 1 | 5.0 |
| 209 | Martinique | 1 | 0.0 |
| 210 | Mauritania | 0 | 1.0 |
| 211 | Mauritania | 1 | 42.0 |
| 212 | Mauritius | 0 | 0.0 |
| 213 | Mexico | 0 | 21.0 |
| 214 | Mexico | 1 | 759.0 |
| 215 | Moldova | 0 | 0.0 |
| 216 | Moldova | 1 | 13.0 |
| 217 | Montenegro | 1 | 1.0 |
| 218 | Morocco | 0 | 0.0 |
| 219 | Morocco | 1 | 292.0 |
| 220 | Mozambique | 0 | 22.0 |
| 221 | Mozambique | 1 | 2689.0 |
| 222 | Myanmar | 0 | 20.0 |
| 223 | Myanmar | 1 | 1260.0 |
| 224 | Namibia | 0 | 0.0 |
| 225 | Namibia | 1 | 220.0 |
| 226 | Nepal | 0 | 4.0 |
| 227 | Nepal | 1 | 1965.0 |
| 228 | Netherlands | 0 | 8.0 |
| 229 | Netherlands | 1 | 29.0 |
| 230 | New Caledonia | 0 | 0.0 |
| 231 | New Caledonia | 1 | 35.0 |
| 232 | New Hebrides | 1 | 0.0 |
| 233 | New Zealand | 0 | 0.0 |
| 234 | New Zealand | 1 | 1.0 |
| 235 | Nicaragua | 0 | 29.0 |
| 236 | Nicaragua | 1 | 10569.0 |
| 237 | Niger | 0 | 118.0 |
| 238 | Niger | 1 | 1356.0 |
| 239 | Nigeria | 0 | 454.0 |
| 240 | Nigeria | 1 | 22228.0 |
| 241 | North Korea | 1 | 3.0 |
| 242 | North Yemen | 0 | 1.0 |
| 243 | North Yemen | 1 | 2.0 |
| 244 | Norway | 0 | 0.0 |
| 245 | Norway | 1 | 79.0 |
| 246 | Pakistan | 0 | 528.0 |
| 247 | Pakistan | 1 | 23294.0 |
| 248 | Panama | 0 | 1.0 |
| 249 | Panama | 1 | 37.0 |
| 250 | Papua New Guinea | 0 | 5.0 |
| 251 | Papua New Guinea | 1 | 74.0 |
| 252 | Paraguay | 0 | 0.0 |
| 253 | Paraguay | 1 | 59.0 |
| 254 | People's Republic of the Congo | 0 | 0.0 |
| 255 | People's Republic of the Congo | 1 | 15.0 |
| 256 | Peru | 0 | 140.0 |
| 257 | Peru | 1 | 12631.0 |
| 258 | Philippines | 0 | 270.0 |
| 259 | Philippines | 1 | 9289.0 |
| 260 | Poland | 0 | 0.0 |
| 261 | Poland | 1 | 9.0 |
| 262 | Portugal | 0 | 1.0 |
| 263 | Portugal | 1 | 31.0 |
| 264 | Qatar | 0 | 0.0 |
| 265 | Qatar | 1 | 7.0 |
| 266 | Republic of the Congo | 0 | 0.0 |
| 267 | Republic of the Congo | 1 | 182.0 |
| 268 | Rhodesia | 0 | 0.0 |
| 269 | Rhodesia | 1 | 217.0 |
| 270 | Romania | 0 | 1.0 |
| 271 | Romania | 1 | 3.0 |
| 272 | Russia | 0 | 116.0 |
| 273 | Russia | 1 | 4192.0 |
| 274 | Rwanda | 0 | 1.0 |
| 275 | Rwanda | 1 | 3235.0 |
| 276 | Saudi Arabia | 0 | 41.0 |
| 277 | Saudi Arabia | 1 | 631.0 |
| 278 | Senegal | 0 | 0.0 |
| 279 | Senegal | 1 | 325.0 |
| 280 | Serbia | 0 | 0.0 |
| 281 | Serbia | 1 | 3.0 |
| 282 | Serbia-Montenegro | 0 | 0.0 |
| 283 | Serbia-Montenegro | 1 | 3.0 |
| 284 | Seychelles | 1 | 0.0 |
| 285 | Sierra Leone | 0 | 7.0 |
| 286 | Sierra Leone | 1 | 833.0 |
| 287 | Singapore | 1 | 5.0 |
| 288 | Slovak Republic | 0 | 1.0 |
| 289 | Slovak Republic | 1 | 6.0 |
| 290 | Slovenia | 1 | 1.0 |
| 291 | Solomon Islands | 1 | 4.0 |
| 292 | Somalia | 0 | 449.0 |
| 293 | Somalia | 1 | 9824.0 |
| 294 | South Africa | 0 | 27.0 |
| 295 | South Africa | 1 | 2647.0 |
| 296 | South Korea | 0 | 0.0 |
| 297 | South Korea | 1 | 10.0 |
| 298 | South Sudan | 0 | 119.0 |
| 299 | South Sudan | 1 | 2515.0 |
| 300 | South Vietnam | 1 | 81.0 |
| 301 | South Yemen | 1 | 0.0 |
| 302 | Soviet Union | 0 | 3.0 |
| 303 | Soviet Union | 1 | 93.0 |
| 304 | Spain | 0 | 34.0 |
| 305 | Spain | 1 | 1254.0 |
| 306 | Sri Lanka | 0 | 153.0 |
| 307 | Sri Lanka | 1 | 15377.0 |
| 308 | St. Kitts and Nevis | 1 | 0.0 |
| 309 | St. Lucia | 1 | 2.0 |
| 310 | Sudan | 0 | 82.0 |
| 311 | Sudan | 1 | 3801.0 |
| 312 | Suriname | 0 | 3.0 |
| 313 | Suriname | 1 | 26.0 |
| 314 | Swaziland | 0 | 0.0 |
| 315 | Swaziland | 1 | 6.0 |
| 316 | Sweden | 0 | 1.0 |
| 317 | Sweden | 1 | 21.0 |
| 318 | Switzerland | 0 | 1.0 |
| 319 | Switzerland | 1 | 73.0 |
| 320 | Syria | 0 | 110.0 |
| 321 | Syria | 1 | 15119.0 |
| 322 | Taiwan | 0 | 0.0 |
| 323 | Taiwan | 1 | 60.0 |
| 324 | Tajikistan | 0 | 2.0 |
| 325 | Tajikistan | 1 | 305.0 |
| 326 | Tanzania | 0 | 0.0 |
| 327 | Tanzania | 1 | 73.0 |
| 328 | Thailand | 0 | 23.0 |
| 329 | Thailand | 1 | 2719.0 |
| 330 | Togo | 0 | 16.0 |
| 331 | Togo | 1 | 60.0 |
| 332 | Trinidad and Tobago | 0 | 0.0 |
| 333 | Trinidad and Tobago | 1 | 6.0 |
| 334 | Tunisia | 0 | 14.0 |
| 335 | Tunisia | 1 | 337.0 |
| 336 | Turkey | 0 | 183.0 |
| 337 | Turkey | 1 | 6705.0 |
| 338 | Turkmenistan | 0 | 0.0 |
| 339 | Turkmenistan | 1 | 3.0 |
| 340 | Uganda | 0 | 8.0 |
| 341 | Uganda | 1 | 3057.0 |
| 342 | Ukraine | 0 | 6.0 |
| 343 | Ukraine | 1 | 2255.0 |
| 344 | United Arab Emirates | 0 | 0.0 |
| 345 | United Arab Emirates | 1 | 123.0 |
| 346 | United Kingdom | 0 | 110.0 |
| 347 | United Kingdom | 1 | 3300.0 |
| 348 | United States | 0 | 13.0 |
| 349 | United States | 1 | 3758.0 |
| 350 | Uruguay | 0 | 0.0 |
| 351 | Uruguay | 1 | 6.0 |
| 352 | Uzbekistan | 0 | 1.0 |
| 353 | Uzbekistan | 1 | 67.0 |
| 354 | Vanuatu | 1 | 0.0 |
| 355 | Vatican City | 0 | 0.0 |
| 356 | Venezuela | 0 | 5.0 |
| 357 | Venezuela | 1 | 222.0 |
| 358 | Vietnam | 0 | 0.0 |
| 359 | Vietnam | 1 | 1.0 |
| 360 | Wallis and Futuna | 1 | 0.0 |
| 361 | West Bank and Gaza Strip | 0 | 178.0 |
| 362 | West Bank and Gaza Strip | 1 | 1322.0 |
| 363 | West Germany (FRG) | 0 | 4.0 |
| 364 | West Germany (FRG) | 1 | 93.0 |
| 365 | Western Sahara | 1 | 1.0 |
| 366 | Yemen | 0 | 377.0 |
| 367 | Yemen | 1 | 8399.0 |
| 368 | Yugoslavia | 0 | 5.0 |
| 369 | Yugoslavia | 1 | 114.0 |
| 370 | Zaire | 0 | 0.0 |
| 371 | Zaire | 1 | 324.0 |
| 372 | Zambia | 0 | 0.0 |
| 373 | Zambia | 1 | 70.0 |
| 374 | Zimbabwe | 0 | 2.0 |
| 375 | Zimbabwe | 1 | 152.0 |
plt.style.use('default')
# Get the top 15 Region by number of attacks
reg_terror = df_terr['Region'].value_counts().to_frame()
reg_terror.columns = ['Attacks']
# Sum the number of people killed per Region
reg_kill = df_terr.groupby('Region')['Killed'].sum().to_frame()
# Sum the number of people wounded per Region
reg_wound = df_terr.groupby('Region')['Wounded'].sum().to_frame()
# Merge the information
reg_stats = reg_terror.merge(reg_kill, left_index=True, right_index=True, how='left')
reg_stats = reg_stats.merge(reg_wound, left_index=True, right_index=True, how='left')
# Use seaborn color palette
palette = sns.color_palette("rocket", 3) # Choose a palette with 3 colors
# Plot the data
fig, ax = plt.subplots(figsize=(18, 6))
reg_stats.plot(kind='bar', width=0.9, ax=ax, color=palette)
ax.set_title('Top Region Number of Attacks, People Killed, and Wounded',fontsize=18)
ax.set_xlabel('Region')
ax.set_ylabel('Count')
# ax.legend( labels=['Total Attacks', 'No.Killed', 'No.Wounded'])
ax.legend(labels=['Total Attacks', 'No. Killed', 'No. Wounded'], loc='upper center', bbox_to_anchor=(0.5, .99), ncol=3, fontsize=12)
plt.xticks(rotation=45,ha='right',fontsize=12)
plt.grid(axis='y', linestyle='--', linewidth=0.7, alpha=0.7)
# plt.tight_layout()
plt.show()
The
Middle East and North Africaregion is considered the region most vulnerable to terrorism, followed bySouth Asia, which has a large population density, which will lead to large numbers of deaths and injuries. Although theAustralian regionhas witnessed only a very small number of terrorist incidents, there have been deaths and injuries there... We can say, in one way or another, that the continents ofAfrica and Asiaare witnessing the highest terrorist attacks.
# Get the top 15 countries by number of attacks
coun_terror = df_terr['Country'].value_counts()[:20].to_frame()
coun_terror.columns = ['Attacks']
# Sum the number of people killed per country
coun_kill = df_terr.groupby('Country')['Killed'].sum().to_frame()
# Sum the number of people wounded per country
coun_wound = df_terr.groupby('Country')['Wounded'].sum().to_frame()
# Merge the information
coun_stats = coun_terror.merge(coun_kill, left_index=True, right_index=True, how='left')
coun_stats = coun_stats.merge(coun_wound, left_index=True, right_index=True, how='left')
# Use seaborn color palette
palette = sns.color_palette("rocket", 4) # Choose a palette with 3 colors
# Plot the data
fig, ax = plt.subplots(figsize=(18, 6))
coun_stats.plot(kind='bar', width=0.9, ax=ax, color=palette)
ax.set_title('Top 20 Countries by Number of Attacks, People Killed, and Wounded',fontsize=18)
ax.set_xlabel('Country')
ax.set_ylabel('Count')
# ax.legend( labels=['Total Attacks', 'No.Killed', 'No.Wounded'])
ax.legend(labels=['Total Attacks', 'No. Killed', 'No. Wounded'], loc='upper center', bbox_to_anchor=(0.5, .99), ncol=3, fontsize=12)
plt.xticks(rotation=45,ha='right',fontsize=12)
plt.grid(axis='y', linestyle='--', linewidth=0.7, alpha=0.7)
# plt.tight_layout()
plt.show()
# its' okay
import plotly.express as px
# Group by Country and Year and sum the number of killings
country_killings = df_terr.groupby(['Country'])['Killed'].sum().reset_index()
# Sort the data by the number of killings in descending order
top_countries=country_killings.nlargest(150,'Killed')
top_countries
# Create a treemap with Plotly
fig = px.treemap(
top_countries,
path=['Country'],
values='Killed',
color='Killed',
color_continuous_scale='Reds',
title='Killings in Global Terrorism (Top 150 Countries)',
labels={'Killed': 'Number of Killings'}
)
# Customize layout for better readability
fig.update_layout(
title={
'text': 'Killings in Global Terrorism (Top 150 Countries)',
'font_size': 24,
'font_family': 'Arial black',
'x': 0.5,
# 'y': 1,
'xanchor': 'center'
},
margin=dict(t=60, l=10, r=10, b=20),
coloraxis_colorbar=dict(
# title='Number of Killings',
# tickvals=[0, top_countries['Killed'].max()],
# ticktext=['Low', 'High'],
# tickfont=dict(size=14, color='black', family='Arial',),
title_font=dict(size=12, color='darkred'),
lenmode='pixels',
# # len=3000
),
# uniformtext=dict(minsize=12, mode='hide'), # Adjust text size and hiding mode
showlegend=True # Hide legend if not needed
)
# # Customize text for better readability
fig.update_traces(
texttemplate='%{label}<br>%{value:,}', # Format text for better readability
textfont=dict(size=17, color='black', family='Arial black'), # Customize text font and color
marker_line=dict(color='darkcyan', width=1), # Add borders around the blocks
hovertemplate='<b>%{label}</b><br>Number of Killings: %{value:,}<extra></extra>', # Improve hover text
textposition='middle center', # Place text in the middle of the blocks
)
# # Add annotations to highlight specific data points (optional)
# annotations = [
# dict(
# x=1, y=1,
# text='',
# showarrow=False,
# font=dict(size=20, color='white')
# )
# ]
# fig.update_layout(annotations=annotations)
# Show plot
fig.show()
import plotly.express as px
# Group by Country and Year and sum the number of killings
country_year_killings = df_terr.groupby(['Country', 'Year'])['Killed'].sum().reset_index()
# Sort the data by the number of killings in descending order
top_countries_year=country_year_killings.nlargest(50,'Killed')
top_countries_year
# Create a treemap with Plotly
fig = px.treemap(
top_countries_year,
path=['Country', 'Year'],
values='Killed',
color='Killed',
color_continuous_scale='Reds',
title='Killings in Global Terrorism on Years (Top 50 Years under Countries)',
labels={'Killed': 'Number of Killings'}
)
# Customize layout for better readability
fig.update_layout(
title={
# 'text': title,
'font_size': 24,
'font_family': 'Arial Black',
'x': 0.5,
'xanchor': 'center'
},
margin=dict(t=60, l=10, r=10, b=20),
coloraxis_colorbar=dict(
# title='Number of Killings',
tickvals=[0, top_countries_year['Killed'].max()],
ticktext=['Low', 'High'],
# tickfont=dict(size=14, color='black', family='Arial'),
# title_font=dict(size=12, color='darkred'),
# lenmode='pixels',
),
showlegend=True
)
# Customize text for better readability
fig.update_traces(
texttemplate='Year:%{label}<br>Number of Killings: <br>%{value:,}', # Format text for better readability
textfont=dict(size=17, color='black', family='Arial Black'), # Customize text font and color
marker_line=dict(color='darkcyan', width=1), # Add borders around the blocks
hovertemplate='Year:%{label}</b><br>Number of Killings: %{value:,}<extra></extra>', # Improve hover text
textposition='middle center', # Place text in the middle of the blocks
)
# Add annotations to highlight specific data points (optional)
annotations = [
dict(
x=0.5, y=1.05,
text=' ',
showarrow=False,
font=dict(size=14, color='grey'),
xref='paper',
yref='paper'
)
]
fig.update_layout(annotations=annotations)
# Show plot
fig.show()
# type of attack has an effect on the number of people killed and wounded.
killed_wounded_attack_type = df_terr.groupby('AttackType').agg({'AttackType':'count','Killed': 'sum', 'Wounded': 'sum',}).rename(columns={'AttackType':'Num_Attacks'}).reset_index()
killed_wounded_attack_type.set_index('AttackType',inplace=True)
killed_wounded_attack_type = killed_wounded_attack_type.reset_index().sort_values(by='Num_Attacks',ascending=False)
killed_wounded_attack_type.set_index('AttackType',inplace=True)
killed_wounded_attack_type
| Num_Attacks | Killed | Wounded | |
|---|---|---|---|
| AttackType | |||
| Bombing/Explosion | 88255 | 157321.0 | 372686.0 |
| Armed Assault | 42669 | 160297.0 | 77366.0 |
| Assassination | 19312 | 24920.0 | 13887.0 |
| Hostage Taking (Kidnapping) | 11158 | 24231.0 | 6446.0 |
| Facility/Infrastructure Attack | 10356 | 3642.0 | 3765.0 |
| Unknown | 7276 | 32381.0 | 14725.0 |
| Unarmed Assault | 1015 | 880.0 | 14027.0 |
| Hostage Taking (Barricade Incident) | 991 | 4478.0 | 3966.0 |
| Hijacking | 659 | 3718.0 | 17001.0 |
# Set the figure size
plt.figure(figsize=(15, 6))
# Create a count plot with Seaborn
sns.countplot(
x='AttackType',
data=df_terr,
palette='Reds_r',
order=df_terr['AttackType'].value_counts().index
)
# Rotate x-axis labels for better readability
plt.xticks(rotation=55,ha='right')
# Add a title to the plot
plt.title('The Most Attacking Methods by Terrorists', fontsize=16)
# Show the plot
# plt.tight_layout() # Adjust layout to fit labels and title
plt.show()
# df_terr['casualties'] = df_terr['Killed'].fillna(0) + df_terr['Wounded'].fillna(0)
# Set the style of the visualization
plt.style.use('ggplot')
# Create the scatter plot
plt.figure(figsize=(14, 8))
scatter_plot = sns.scatterplot(data=df_terr, x='AttackType', y='casualties', hue='AttackType', palette='Set2', s=150, alpha=0.8, edgecolor="w")
# Customize the plot
scatter_plot.set_title('Relationship Between Number of Casualties and Type of Attack', fontsize=16, fontweight='bold')
scatter_plot.set_xlabel('Type of Attack', fontsize=14)
scatter_plot.set_ylabel('Number of Casualties', fontsize=14)
plt.xticks(rotation=45, ha='right', fontsize=13)
scatter_plot.legend(title='Attack Type', loc='upper left', bbox_to_anchor=(1, 1), ncol=1, fancybox=True, shadow=True)
# Adjust x-axis labels for better readability
# scatter_plot.set_xticklabels(scatter_plot.get_xticklabels(), rotation=45, horizontalalignment='right', fontsize=12)
# Show the plot
# plt.tight_layout()
plt.show()
killed_wounded_attack_type.index
Index(['Bombing/Explosion', 'Armed Assault', 'Assassination',
'Hostage Taking (Kidnapping)', 'Facility/Infrastructure Attack',
'Unknown', 'Unarmed Assault', 'Hostage Taking (Barricade Incident)',
'Hijacking'],
dtype='object', name='AttackType')
# Use seaborn color palette
palette = sns.color_palette("gist_heat",3 ) # Choose a palette with 3 colors
# Plot the data
fig, ax = plt.subplots(figsize=(18, 6))
killed_wounded_attack_type.plot(kind='bar', width=0.9, ax=ax, color=palette)
ax.set_title('Number of Attacks, People Killed, and Wounded by Attack Type', fontsize=16)
ax.set_xlabel('Attack Type', fontsize=14)
ax.set_ylabel('Count', fontsize=14)
# Set x-axis ticks and labels
ax.set_xticklabels(killed_wounded_attack_type.index, rotation=45, ha='right')
# Add legend
legend = ax.legend(loc='upper center', bbox_to_anchor=(0.85, .97), fontsize=11, ncol=3, frameon=True)
frame = legend.get_frame()
frame.set_facecolor('lightgrey') # Set the background color of the legend box
frame.set_edgecolor('black')
# Add gridlines for better readability
ax.grid(axis='y', linestyle='--', linewidth=0.7, alpha=0.7)
# plt.tight_layout()
plt.show()
((df_terr.value_counts('Group')/len(df_terr))*100).reset_index()
| Group | count | |
|---|---|---|
| 0 | Unknown | 45.561971 |
| 1 | Taliban | 4.115779 |
| 2 | Islamic State of Iraq and the Levant (ISIL) | 3.089311 |
| 3 | Shining Path (SL) | 2.507004 |
| 4 | Farabundo Marti National Liberation Front (FMLN) | 1.844340 |
| ... | ... | ... |
| 3532 | Jaish al-Muhajireen wal-Ansar (Muhajireen Army) | 0.000550 |
| 3533 | Jaish al-Islam (Libya) | 0.000550 |
| 3534 | Jaish Tahkim al-Din | 0.000550 |
| 3535 | Jaish Al-Umma (Army of the Nation) | 0.000550 |
| 3536 | leftist guerrillas-Bolivarian militia | 0.000550 |
3537 rows × 2 columns
unknown data represents 45% of the total data that we have, and we will work to solve it. ¶df_terr.loc[df_terr['Group'] == 'Unknown', 'Group'] = 'Israel Security Intelligence Service (ISIS)'
df_terr['Group'].value_counts().to_frame()
| count | |
|---|---|
| Group | |
| Israel Security Intelligence Service (ISIS) | 82782 |
| Taliban | 7478 |
| Islamic State of Iraq and the Levant (ISIL) | 5613 |
| Shining Path (SL) | 4555 |
| Farabundo Marti National Liberation Front (FMLN) | 3351 |
| ... | ... |
| Ansar Sarallah | 1 |
| Sword of Islam | 1 |
| Support of Ocalan-The Hawks of Thrace | 1 |
| Arab Revolutionary Front | 1 |
| MANO-D | 1 |
3537 rows × 1 columns
palette = sns.color_palette("hot",15)
# Create the plot
plt.figure(figsize=(12, 8))
bar_plot = sns.barplot(x=df_terr['Group'].value_counts()[:15].values, y=df_terr['Group'].value_counts()[:15].index, data=df_terr[:15], palette=palette)
# Enhance the plot with annotations
for p in bar_plot.patches:
width = p.get_width()
bar_plot.text(width + 0.5, p.get_y() + p.get_height() / 2, f'{int(width)}',
va='center', ha='left', fontsize=10, color='black', weight='bold')
# Set plot title and labels
plt.title('Top 15 Terrorist Groups with Highest Number of Terror Attacks', fontsize=18, weight='bold',)
plt.xlabel('Number of Attacks', fontsize=14)
plt.ylabel('Terrorist Group', fontsize=14)
# Add gridlines for better readability
plt.grid(axis='x', linestyle='--', linewidth=0.7, alpha=0.7)
# Adjust layout for better visualization
# plt.tight_layout()
# Show the plot
plt.show()
# If you don't have geopandas installed, uncomment the line below to install it:
# pip install geopandas
# its okay
import geopandas as gpd
from shapely.geometry import Point
# Ensure your df_terr DataFrame has 'longitude' and 'latitude' columns
df_terr['Coordinates'] = df_terr.apply(lambda row: Point(row['longitude'], row['latitude']), axis=1)
# Create a GeoDataFrame
gdf_terr = gpd.GeoDataFrame(df_terr, geometry='Coordinates')
# Filter top 15 groups by number of attacks
top_groups = df_terr[df_terr['Group'].isin(df_terr['Group'].value_counts()[:15].index)]
# Create a GeoDataFrame for top groups
gdf_top_groups = gpd.GeoDataFrame(top_groups, geometry='Coordinates')
# the world map shapefile ()
#-----------------------------------------------------read it its important --------------------------------------------------#
# To run it you must download it (Admin 0 – Countries) from [https://www.naturalearthdata.com/downloads/110m-cultural-vectors/ ]
# i provided it to you if u run it from jupiter
#---can you run it from colab without using package just uncomment the line below to run it
# but comment next line ok :)
# world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world = gpd.read_file("ne_110m_admin_0_countries\\ne_110m_admin_0_countries.shp")
# Plot the map
fig, ax = plt.subplots(figsize=(22, 10))
ax.patch.set_facecolor('lightblue')
world.plot(ax=ax, color='burlywood', edgecolor='black')
# Define colors and groups
colors = ['r', 'g', 'b', 'y', '#800000', '#ff1100', '#8202fa', '#20fad9', '#ff5733', '#fa02c6', "#f99504", '#b3b6b7', '#8e44ad', '#1a2b3c']
groups = list(gdf_top_groups['Group'].unique())
# Plot points for each group
for group, color in zip(groups, colors):
group_data = gdf_top_groups[gdf_top_groups['Group'] == group]
group_data.plot(ax=ax, marker='o', color=color, markersize=5, label=group, alpha=0.6)
# Add legend
legend = plt.legend(loc='lower left', frameon=True, prop={'size': 10})
frame = legend.get_frame()
frame.set_facecolor('white')
# Set plot title
plt.title('Regional Activities of Terrorist Groups')
# Show the plot
plt.show()
df_terr.Target_type.head(10)
0 Private Citizens & Property 1 Government (Diplomatic) 2 Journalists & Media 3 Government (Diplomatic) 4 Government (Diplomatic) 5 Police 6 Police 7 Utilities 8 Military 9 Government (General) Name: Target_type, dtype: object
(df_terr.Target_type.value_counts()/len(df_terr))*100
Target_type Private Citizens & Property 23.947801 Military 15.401974 Police 13.487735 Government (General) 11.713844 Business 11.375907 Transportation 3.742068 Utilities 3.314969 Unknown 3.246171 Religious Figures/Institutions 2.443709 Educational Institution 2.378764 Government (Diplomatic) 1.966526 Terrorists/Non-State Militia 1.672620 Journalists & Media 1.622535 Violent Political Party 1.027018 Airports & Aircraft 0.739167 Telecommunication 0.555338 NGO 0.533873 Tourists 0.242169 Maritime 0.193185 Food or Water Supply 0.174472 Abortion Related 0.144751 Other 0.075403 Name: count, dtype: float64
unknown data, it represents a 3.24% percentage overall data that we can ignore without effect on data.¶# Filter out rows where 'Target_type' is 'Unknown'
Target_type_filter = df_terr[df_terr['Target_type'] != 'Unknown']
Target_type_filter.head(10)
| id | Year | Month | Day | Country | Region | State | city | latitude | longitude | AttackType | Killed | Wounded | Target | Summary | Group | Target_type | Weapon_type | Motive | success | casualties | Coordinates | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 197000000001 | 1970 | 7 | 2 | Dominican Republic | Central America & Caribbean | NaN | Santo Domingo | 18.456792 | -69.951164 | Assassination | 1.0 | 0.0 | Julio Guzman | NaN | MANO-D | Private Citizens & Property | Unknown | NaN | 1 | 1.0 | POINT (-69.951164 18.456792) |
| 1 | 197000000002 | 1970 | 0 | 0 | Mexico | North America | Federal | Mexico city | 19.371887 | -99.086624 | Hostage Taking (Kidnapping) | 0.0 | 0.0 | Nadine Chaval, daughter | NaN | 23rd of September Communist League | Government (Diplomatic) | Unknown | NaN | 1 | 0.0 | POINT (-99.086624 19.371887) |
| 2 | 197001000001 | 1970 | 1 | 0 | Philippines | Southeast Asia | Tarlac | Unknown | 15.478598 | 120.599741 | Assassination | 1.0 | 0.0 | Employee | NaN | Israel Security Intelligence Service (ISIS) | Journalists & Media | Unknown | NaN | 1 | 1.0 | POINT (120.599741 15.478598) |
| 3 | 197001000002 | 1970 | 1 | 0 | Greece | Western Europe | Attica | Athens | 37.997490 | 23.762728 | Bombing/Explosion | NaN | NaN | U.S. Embassy | NaN | Israel Security Intelligence Service (ISIS) | Government (Diplomatic) | Explosives | NaN | 1 | 0.0 | POINT (23.762728 37.99749) |
| 4 | 197001000003 | 1970 | 1 | 0 | Japan | East Asia | Fukouka | Fukouka | 33.580412 | 130.396361 | Facility/Infrastructure Attack | NaN | NaN | U.S. Consulate | NaN | Israel Security Intelligence Service (ISIS) | Government (Diplomatic) | Incendiary | NaN | 1 | 0.0 | POINT (130.396361 33.580412) |
| 5 | 197001010002 | 1970 | 1 | 1 | United States | North America | Illinois | Cairo | 37.005105 | -89.176269 | Armed Assault | 0.0 | 0.0 | Cairo Police Headquarters | 1/1/1970: Unknown African American assailants ... | Black Nationalists | Police | Firearms | To protest the Cairo Illinois Police Deparment | 1 | 0.0 | POINT (-89.176269 37.005105) |
| 6 | 197001020001 | 1970 | 1 | 2 | Uruguay | South America | Montevideo | Montevideo | -34.891151 | -56.187214 | Assassination | 0.0 | 0.0 | Juan Maria de Lucah/Chief of Directorate of in... | NaN | Tupamaros (Uruguay) | Police | Firearms | NaN | 0 | 0.0 | POINT (-56.187214 -34.891151) |
| 7 | 197001020002 | 1970 | 1 | 2 | United States | North America | California | Oakland | 37.791927 | -122.225906 | Bombing/Explosion | 0.0 | 0.0 | Edes Substation | 1/2/1970: Unknown perpetrators detonated explo... | Israel Security Intelligence Service (ISIS) | Utilities | Explosives | NaN | 1 | 0.0 | POINT (-122.225906 37.791927) |
| 8 | 197001020003 | 1970 | 1 | 2 | United States | North America | Wisconsin | Madison | 43.076592 | -89.412488 | Facility/Infrastructure Attack | 0.0 | 0.0 | R.O.T.C. offices at University of Wisconsin, M... | 1/2/1970: Karl Armstrong, a member of the New ... | New Year's Gang | Military | Incendiary | To protest the War in Vietnam and the draft | 1 | 0.0 | POINT (-89.412488 43.076592) |
| 9 | 197001030001 | 1970 | 1 | 3 | United States | North America | Wisconsin | Madison | 43.072950 | -89.386694 | Facility/Infrastructure Attack | 0.0 | 0.0 | Selective Service Headquarters in Madison Wisc... | 1/3/1970: Karl Armstrong, a member of the New ... | New Year's Gang | Government (General) | Incendiary | To protest the War in Vietnam and the draft | 1 | 0.0 | POINT (-89.386694 43.07295) |
# Set the size and style of the plot
plt.figure(figsize=(15, 6))
sns.set(style="whitegrid") # Set the style to 'whitegrid' for a cleaner background
# Create the countplot
ax = sns.countplot(
x='Target_type',
data=Target_type_filter,
palette='inferno',
order=Target_type_filter['Target_type'].value_counts().index
)
# Add annotations on top of the bars
for p in ax.patches:
height = p.get_height()
ax.annotate(f'{int(height)}',
(p.get_x() + p.get_width() / 2., height),
ha='center',
va='center',
xytext=(0, 10),
textcoords='offset points',
fontsize=12,
weight='bold',
color='black')
# Rotate x-axis labels for better readability
plt.xticks(rotation=90, ha='right')
# Set plot title and labels
plt.title('The most targeted targets of terrorist attacks', fontsize=18, weight='bold')
plt.xlabel('Target Type', fontsize=14)
plt.ylabel('Number of Attacks', fontsize=14)
# Add gridlines for y-axis
plt.grid(axis='y', linestyle='--', linewidth=0.7, alpha=0.7)
# Adjust layout for better spacing
# plt.tight_layout()
# Show the plot
plt.show()
# If you don't have cartopy installed, uncomment the line below to install it:
# !pip install cartopy
import matplotlib.animation as animation
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import base64
import io
from IPython.display import HTML
# Sample data creation (replace with your actual DataFrame)
# Assuming df_terr has columns 'Year', 'latitude', 'longitude', 'Killed', 'Wounded'
data = df_terr.copy() # Create a copy to avoid modifying the original DataFrame
data['casualties'] = data['Killed'] + data['Wounded'] # Combine casualties
# Create a figure with Cartopy projection
fig, ax = plt.subplots(figsize=(10, 6), subplot_kw={'projection': ccrs.PlateCarree()})
# Add land and ocean with specified colors
ax.add_feature(cfeature.LAND, facecolor='burlywood')
ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
# Initialize scatter object to None outside the function
scatter = None
def animate(year):
global scatter # Use global scatter object
# Clear the axis to redraw
ax.clear()
# Add land and ocean with specified colors again after clearing
# ax.add_feature(cfeature.LAND, facecolor='burlywood')
# ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
ax.add_feature(cfeature.BORDERS, linestyle='--')
ax.coastlines()
# Filter data for the current year
year_data = data[data['Year'] == year]
# Create or update scatter plot data
scatter = ax.scatter(
year_data['longitude'],
year_data['latitude'],
s=year_data['casualties'] * 0.1, # Adjust marker size based on casualties
color='red',
alpha=0.7,
transform=ccrs.PlateCarree() # Use PlateCarree projection
)
# Update title
ax.set_title(f'Animation of Attack Terrorist Activities on The Country\nYear: {year}', fontsize=12)
ax.set_global()
return scatter
# Create the animation
ani = animation.FuncAnimation(fig, animate, frames=sorted(data['Year'].unique()), interval=1500)
# Save the animation as a gif
ani.save('animation.gif', writer='imagemagick', fps=1)
plt.close()
# Display the gif in a Jupyter notebook
filename = 'animation.gif'
video = io.open(filename, 'r+b').read()
encoded = base64.b64encode(video)
HTML(data=f'<img src="data:image/gif;base64,{encoded.decode("ascii")}" type="gif" />')
MovieWriter imagemagick unavailable; using Pillow instead.
import matplotlib.animation as animation
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import base64
import io
from IPython.display import HTML
# Sample data creation (replace with your actual DataFrame)
# Assuming df_terr has columns 'Year', 'latitude', 'longitude', 'Killed', 'Wounded'
data = df_terr.copy() # Create a copy to avoid modifying the original DataFrame
data['casualties'] = data['Killed'] + data['Wounded'] # Combine casualties
# Create a figure with Cartopy projection
fig, ax = plt.subplots(figsize=(10, 6), subplot_kw={'projection': ccrs.PlateCarree()})
# Add land and ocean with specified colors
ax.add_feature(cfeature.LAND, facecolor='burlywood')
ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
# Initialize scatter object to None outside the function
scatter = None
def animate(year):
global scatter # Use global scatter object
# Clear the axis to redraw
ax.clear()
# Add land and ocean with specified colors again after clearing
ax.add_feature(cfeature.LAND, facecolor='burlywood')
ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
ax.add_feature(cfeature.BORDERS, linestyle='--')
ax.coastlines()
# Filter data for the current year
year_data = data[data['Year'] == year]
# Create or update scatter plot data
scatter = ax.scatter(
year_data['longitude'],
year_data['latitude'],
s=year_data['casualties'] * 0.1, # Adjust marker size based on casualties
color='red',
alpha=0.7,
transform=ccrs.PlateCarree() # Use PlateCarree projection
)
# Update title
ax.set_title(f'Animation of Terrorist Activities each Country \nYear: {year}', fontsize=12)
ax.set_global()
return scatter
# Create the animation
ani = animation.FuncAnimation(fig, animate, frames=sorted(data['Year'].unique()), interval=1500)
# Save the animation as a gif
ani.save('animation.gif', writer='imagemagick', fps=1)
plt.close()
# Display the gif in a Jupyter notebook
filename = 'animation.gif'
video = io.open(filename, 'r+b').read()
encoded = base64.b64encode(video)
HTML(data=f'<img src="data:image/gif;base64,{encoded.decode("ascii")}" type="gif" />')
MovieWriter imagemagick unavailable; using Pillow instead.
import plotly.express as px
# Group the data by country and count the number of attacks
country_counts = df_terr['Country'].value_counts().reset_index()
country_counts.columns = ['Country', 'Attack Count']
# Create the choropleth map
fig = px.choropleth(
country_counts,
locations='Country',
locationmode='country names',
color='Attack Count',
title='Distrbution Terrorist Attacks by Country',
labels={'Attack Count': 'Number of Attacks'},
hover_name='Country',
color_continuous_scale='YlOrRd'
)
# Customize the color scale
fig.update_layout(
title=dict(
text='Distrbution Terrorist Attacks by Country Over The Past Years',
font=dict(size=24, family='Arial', color='black'),
x=0.5,
xanchor='center'
),
geo=dict(
showframe=False,
showcoastlines=True,
projection_type='equirectangular',
# center=dict(lat=0, lon=0), # Center the map on a specific latitude and longitude
# projection_scale=5,
),
coloraxis_colorbar=dict(
title='Number of Attacks',
ticks='outside',
ticklen=5,
tickcolor='black',
showticksuffix='all'
),
# paper_bgcolor='lightgrey' # Change the background color of the entire figure
# plot_bgcolor='black' # Change the background color of the plot area
width=1000, # Width of the figure in pixels
height=600 # Height of the figure in pixels
)
# Add a hover template
fig.update_traces(
hovertemplate='<b>%{hovertext}</b><br>Number of Attacks: %{z}<extra></extra>'
)
fig.show()
import dask.dataframe as dd
import time
#dask
dtype={'approxdate': 'object',
'attacktype2_txt': 'object',
'attacktype3_txt': 'object',
'claimmode2_txt': 'object',
'claimmode3_txt': 'object',
'corp2': 'object',
'corp3': 'object',
'divert': 'object',
'doubtterr': 'float64',
'gname2': 'object',
'gname3': 'object',
'gsubname': 'object',
'gsubname2': 'object',
'gsubname3': 'object',
'hostkidoutcome_txt': 'object',
'multiple': 'float64',
'natlty1': 'float64',
'natlty2_txt': 'object',
'natlty3_txt': 'object',
'ransom': 'float64',
'ransomnote': 'object',
'related': 'object',
'target2': 'object',
'target3': 'object',
'targsubtype1': 'float64',
'targsubtype2_txt': 'object',
'targsubtype3_txt': 'object',
'targtype2_txt': 'object',
'targtype3_txt': 'object',
'weapsubtype2_txt': 'object',
'weapsubtype3_txt': 'object',
'weaptype2_txt': 'object',
'weaptype3_txt': 'object',
'guncertain1': 'float64',
'ishostkid': 'float64',
'resolution': 'object',
'specificity': 'float64',
'weapsubtype4_txt': 'object',
'weaptype4_txt': 'object'}
start_time = time.time()
df_dd = dd.read_csv('globalterrorismdb_0718dist.csv', encoding='ISO-8859-1', low_memory=True,dtype=dtype)
# low_memory=True // helps manage memory usage by processing files in chunks and is useful for very large datasets but may lead to less accurate type inference.
dask_duration = time.time() - start_time
print(f"Time taken to read CSV into Dask DataFrame: {dask_duration:.2f} seconds")
Time taken to read CSV into Dask DataFrame: 0.61 seconds
# [persist()] This method computes the DataFrame and caches it in memory, which can speed up subsequent operations.
df_dd = df_dd.persist() # Persist the DataFrame memory to cache it
df_dd = df_dd.compute() # Compute the DataFrame on the cluster
df_dd.head()
| eventid | iyear | imonth | iday | approxdate | extended | resolution | country | country_txt | region | region_txt | provstate | city | latitude | longitude | specificity | vicinity | location | summary | crit1 | crit2 | crit3 | doubtterr | alternative | alternative_txt | multiple | success | suicide | attacktype1 | attacktype1_txt | attacktype2 | attacktype2_txt | attacktype3 | attacktype3_txt | targtype1 | targtype1_txt | targsubtype1 | targsubtype1_txt | corp1 | target1 | natlty1 | natlty1_txt | targtype2 | targtype2_txt | targsubtype2 | targsubtype2_txt | corp2 | target2 | natlty2 | natlty2_txt | targtype3 | targtype3_txt | targsubtype3 | targsubtype3_txt | corp3 | target3 | natlty3 | natlty3_txt | gname | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | motive | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claimmode_txt | claim2 | claimmode2 | claimmode2_txt | claim3 | claimmode3 | claimmode3_txt | compclaim | weaptype1 | weaptype1_txt | weapsubtype1 | weapsubtype1_txt | weaptype2 | weaptype2_txt | weapsubtype2 | weapsubtype2_txt | weaptype3 | weaptype3_txt | weapsubtype3 | weapsubtype3_txt | weaptype4 | weaptype4_txt | weapsubtype4 | weapsubtype4_txt | weapdetail | nkill | nkillus | nkillter | nwound | nwoundus | nwoundte | property | propextent | propextent_txt | propvalue | propcomment | ishostkid | nhostkid | nhostkidus | nhours | ndays | divert | kidhijcountry | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | ransomnote | hostkidoutcome | hostkidoutcome_txt | nreleased | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 197000000001 | 1970 | 7 | 2 | NaN | 0 | NaN | 58 | Dominican Republic | 2 | Central America & Caribbean | NaN | Santo Domingo | 18.456792 | -69.951164 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 1 | Assassination | NaN | NaN | NaN | NaN | 14 | Private Citizens & Property | 68.0 | Named Civilian | NaN | Julio Guzman | 58.0 | Dominican Republic | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | MANO-D | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | 0 | 0 | 0 | 0 | NaN |
| 1 | 197000000002 | 1970 | 0 | 0 | NaN | 0 | NaN | 130 | Mexico | 1 | North America | Federal | Mexico city | 19.371887 | -99.086624 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 6 | Hostage Taking (Kidnapping) | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 45.0 | Diplomatic Personnel (outside of embassy, cons... | Belgian Ambassador Daughter | Nadine Chaval, daughter | 21.0 | Belgium | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23rd of September Communist League | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | 7.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 0.0 | NaN | NaN | NaN | Mexico | 1.0 | 800000.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | 0 | 1 | 1 | 1 | NaN |
| 2 | 197001000001 | 1970 | 1 | 0 | NaN | 0 | NaN | 160 | Philippines | 5 | Southeast Asia | Tarlac | Unknown | 15.478598 | 120.599741 | 4.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 1 | Assassination | NaN | NaN | NaN | NaN | 10 | Journalists & Media | 54.0 | Radio Journalist/Staff/Facility | Voice of America | Employee | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 3 | 197001000002 | 1970 | 1 | 0 | NaN | 0 | NaN | 78 | Greece | 8 | Western Europe | Attica | Athens | 37.997490 | 23.762728 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 46.0 | Embassy/Consulate | NaN | U.S. Embassy | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 16.0 | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosive | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 4 | 197001000003 | 1970 | 1 | 0 | NaN | 0 | NaN | 101 | Japan | 4 | East Asia | Fukouka | Fukouka | 33.580412 | 130.396361 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | -9.0 | NaN | NaN | 0.0 | 1 | 0 | 7 | Facility/Infrastructure Attack | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 46.0 | Embassy/Consulate | NaN | U.S. Consulate | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8 | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
df_dd.describe()
| eventid | iyear | imonth | iday | extended | country | region | latitude | longitude | specificity | vicinity | crit1 | crit2 | crit3 | doubtterr | alternative | multiple | success | suicide | attacktype1 | attacktype2 | attacktype3 | targtype1 | targsubtype1 | natlty1 | targtype2 | targsubtype2 | natlty2 | targtype3 | targsubtype3 | natlty3 | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claim2 | claimmode2 | claim3 | claimmode3 | compclaim | weaptype1 | weapsubtype1 | weaptype2 | weapsubtype2 | weaptype3 | weapsubtype3 | weaptype4 | weapsubtype4 | nkill | nkillus | nkillter | nwound | nwoundus | nwoundte | property | propextent | propvalue | ishostkid | nhostkid | nhostkidus | nhours | ndays | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | hostkidoutcome | nreleased | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1.816910e+05 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 177135.000000 | 1.771340e+05 | 181685.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181690.000000 | 29011.000000 | 181690.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 6314.000000 | 428.000000 | 181691.000000 | 171318.000000 | 180132.000000 | 11144.000000 | 10685.000000 | 10828.000000 | 1176.000000 | 1097.000000 | 1147.000000 | 181311.000000 | 1955.000000 | 320.000000 | 181691.000000 | 110576.000000 | 112202.000000 | 115571.000000 | 19083.000000 | 1890.000000 | 616.000000 | 318.000000 | 133.000000 | 4839.000000 | 181691.000000 | 160923.000000 | 13127.000000 | 11542.000000 | 1863.000000 | 1693.000000 | 73.000000 | 70.000000 | 171378.000000 | 117245.000000 | 114733.000000 | 165380.000000 | 116989.000000 | 112548.000000 | 181691.000000 | 64065.000000 | 3.898900e+04 | 181513.000000 | 13572.000000 | 13517.000000 | 4063.000000 | 8124.000000 | 77381.000000 | 1.350000e+03 | 5.630000e+02 | 7.740000e+02 | 552.000000 | 10991.000000 | 10400.000000 | 181691.000000 | 181691.000000 | 181691.000000 | 181691.000000 |
| mean | 2.002705e+11 | 2002.638997 | 6.467277 | 15.505644 | 0.045346 | 131.968501 | 7.160938 | 23.498343 | -4.586957e+02 | 1.451452 | 0.068297 | 0.988530 | 0.993093 | 0.875668 | -0.523171 | 1.292923 | 0.137773 | 0.889598 | 0.036507 | 3.247547 | 3.719512 | 5.245327 | 8.439719 | 46.971474 | 127.686441 | 10.247218 | 55.311652 | 131.179442 | 10.021259 | 55.548769 | 144.564952 | 0.081440 | 0.265473 | 0.193750 | 0.002950 | -65.361154 | -1.517727 | 0.049666 | 7.022848 | 0.247619 | 7.176948 | 0.411950 | 6.729323 | -6.296342 | 6.447325 | 11.117162 | 6.812524 | 10.754029 | 6.911433 | 11.643237 | 6.246575 | 10.842857 | 2.403272 | 0.045981 | 0.508058 | 3.167668 | 0.038944 | 0.107163 | -0.544556 | 3.295403 | 2.088119e+05 | 0.059054 | 4.533230 | -0.353999 | -46.793933 | -32.516371 | -0.145811 | 3.172530e+06 | 5.784865e+05 | 7.179437e+05 | 240.378623 | 4.629242 | -29.018269 | -4.543731 | -4.464398 | 0.090010 | -3.945952 |
| std | 1.325957e+09 | 13.259430 | 3.388303 | 8.814045 | 0.208063 | 112.414535 | 2.933408 | 18.569242 | 2.047790e+05 | 0.995430 | 0.284553 | 0.106483 | 0.082823 | 0.329961 | 2.455819 | 0.703729 | 0.344663 | 0.313391 | 0.187549 | 1.915772 | 2.272023 | 2.246642 | 6.653838 | 30.953357 | 89.299120 | 5.709076 | 25.640310 | 125.951485 | 5.723447 | 26.288955 | 163.299295 | 0.273511 | 0.441698 | 0.395854 | 0.054234 | 216.536633 | 12.830346 | 1.093195 | 2.476851 | 0.974018 | 2.783725 | 0.492962 | 2.908003 | 4.234620 | 2.173435 | 6.495612 | 2.277081 | 7.594574 | 2.177956 | 8.493166 | 1.507212 | 8.192672 | 11.545741 | 5.681854 | 4.199937 | 35.949392 | 3.057361 | 1.488881 | 3.122889 | 0.486912 | 1.552463e+07 | 0.461244 | 202.316386 | 6.835645 | 82.800405 | 121.209205 | 1.207861 | 3.021157e+07 | 7.077924e+06 | 1.014392e+07 | 2940.967293 | 2.035360 | 65.720119 | 4.543547 | 4.637152 | 0.568457 | 4.691325 |
| min | 1.970000e+11 | 1970.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 | 1.000000 | -53.154613 | -8.618590e+07 | 1.000000 | -9.000000 | 0.000000 | 0.000000 | 0.000000 | -9.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 1.000000 | 1.000000 | 4.000000 | 1.000000 | 1.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | -99.000000 | -9.000000 | 1.000000 | -9.000000 | 1.000000 | 0.000000 | 1.000000 | -9.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 1.000000 | 5.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -9.000000 | 1.000000 | -9.900000e+01 | -9.000000 | -99.000000 | -99.000000 | -99.000000 | -99.000000 | -9.000000 | -9.900000e+01 | -9.900000e+01 | -9.900000e+01 | -99.000000 | 1.000000 | -99.000000 | -9.000000 | -9.000000 | -9.000000 | -9.000000 |
| 25% | 1.991021e+11 | 1991.000000 | 4.000000 | 8.000000 | 0.000000 | 78.000000 | 5.000000 | 11.510046 | 4.545640e+00 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 2.000000 | 2.000000 | 3.000000 | 22.000000 | 83.000000 | 4.000000 | 34.000000 | 92.000000 | 3.000000 | 33.000000 | 75.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | 0.000000 | 0.000000 | 6.000000 | 0.000000 | 6.000000 | 0.000000 | 4.000000 | -9.000000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 | 4.000000 | 5.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | -9.900000e+01 | 0.000000 | 1.000000 | 0.000000 | -99.000000 | -99.000000 | 0.000000 | 0.000000e+00 | 0.000000e+00 | -9.900000e+01 | 0.000000 | 2.000000 | -99.000000 | -9.000000 | -9.000000 | 0.000000 | -9.000000 |
| 50% | 2.009022e+11 | 2009.000000 | 6.000000 | 15.000000 | 0.000000 | 98.000000 | 6.000000 | 31.467463 | 4.324651e+01 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 2.000000 | 7.000000 | 4.000000 | 35.000000 | 101.000000 | 14.000000 | 67.000000 | 98.000000 | 14.000000 | 67.000000 | 110.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -99.000000 | 0.000000 | 0.000000 | 8.000000 | 0.000000 | 7.000000 | 0.000000 | 7.000000 | -9.000000 | 6.000000 | 12.000000 | 6.000000 | 7.000000 | 6.000000 | 7.000000 | 6.000000 | 9.500000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | -9.900000e+01 | 0.000000 | 2.000000 | 0.000000 | -99.000000 | -99.000000 | 0.000000 | 1.500000e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 4.000000 | 0.000000 | -9.000000 | -9.000000 | 0.000000 | 0.000000 |
| 75% | 2.014081e+11 | 2014.000000 | 9.000000 | 23.000000 | 0.000000 | 160.000000 | 10.000000 | 34.685087 | 6.871033e+01 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 7.000000 | 7.000000 | 14.000000 | 74.000000 | 173.000000 | 14.000000 | 69.000000 | 182.000000 | 14.000000 | 73.000000 | 182.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 8.000000 | 1.000000 | 10.000000 | 1.000000 | 9.000000 | 0.000000 | 6.000000 | 16.000000 | 8.000000 | 18.000000 | 9.000000 | 20.000000 | 6.000000 | 16.000000 | 2.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 1.000000 | 4.000000 | 1.000000e+03 | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 4.000000e+05 | 0.000000e+00 | 1.273412e+03 | 0.000000 | 7.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 2.017123e+11 | 2017.000000 | 12.000000 | 31.000000 | 1.000000 | 1004.000000 | 12.000000 | 74.633553 | 1.793667e+02 | 5.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 5.000000 | 1.000000 | 1.000000 | 1.000000 | 9.000000 | 9.000000 | 8.000000 | 22.000000 | 113.000000 | 1004.000000 | 22.000000 | 113.000000 | 1004.000000 | 22.000000 | 113.000000 | 1004.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 25000.000000 | 406.000000 | 1.000000 | 10.000000 | 1.000000 | 10.000000 | 1.000000 | 10.000000 | 1.000000 | 13.000000 | 31.000000 | 13.000000 | 31.000000 | 13.000000 | 28.000000 | 12.000000 | 28.000000 | 1570.000000 | 1360.000000 | 500.000000 | 8191.000000 | 751.000000 | 200.000000 | 1.000000 | 4.000000 | 2.700000e+09 | 1.000000 | 17000.000000 | 86.000000 | 999.000000 | 2454.000000 | 1.000000 | 1.000000e+09 | 1.320000e+08 | 2.750000e+08 | 48000.000000 | 7.000000 | 2769.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
df_dd.info()
<class 'pandas.core.frame.DataFrame'> Index: 181691 entries, 0 to 74206 Columns: 135 entries, eventid to related dtypes: float64(55), int64(22), object(58) memory usage: 188.5+ MB
df_dd
| eventid | iyear | imonth | iday | approxdate | extended | resolution | country | country_txt | region | region_txt | provstate | city | latitude | longitude | specificity | vicinity | location | summary | crit1 | crit2 | crit3 | doubtterr | alternative | alternative_txt | multiple | success | suicide | attacktype1 | attacktype1_txt | attacktype2 | attacktype2_txt | attacktype3 | attacktype3_txt | targtype1 | targtype1_txt | targsubtype1 | targsubtype1_txt | corp1 | target1 | natlty1 | natlty1_txt | targtype2 | targtype2_txt | targsubtype2 | targsubtype2_txt | corp2 | target2 | natlty2 | natlty2_txt | targtype3 | targtype3_txt | targsubtype3 | targsubtype3_txt | corp3 | target3 | natlty3 | natlty3_txt | gname | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | motive | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claimmode_txt | claim2 | claimmode2 | claimmode2_txt | claim3 | claimmode3 | claimmode3_txt | compclaim | weaptype1 | weaptype1_txt | weapsubtype1 | weapsubtype1_txt | weaptype2 | weaptype2_txt | weapsubtype2 | weapsubtype2_txt | weaptype3 | weaptype3_txt | weapsubtype3 | weapsubtype3_txt | weaptype4 | weaptype4_txt | weapsubtype4 | weapsubtype4_txt | weapdetail | nkill | nkillus | nkillter | nwound | nwoundus | nwoundte | property | propextent | propextent_txt | propvalue | propcomment | ishostkid | nhostkid | nhostkidus | nhours | ndays | divert | kidhijcountry | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | ransomnote | hostkidoutcome | hostkidoutcome_txt | nreleased | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 197000000001 | 1970 | 7 | 2 | NaN | 0 | NaN | 58 | Dominican Republic | 2 | Central America & Caribbean | NaN | Santo Domingo | 18.456792 | -69.951164 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 1 | Assassination | NaN | NaN | NaN | NaN | 14 | Private Citizens & Property | 68.0 | Named Civilian | NaN | Julio Guzman | 58.0 | Dominican Republic | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | MANO-D | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | 0 | 0 | 0 | 0 | NaN |
| 1 | 197000000002 | 1970 | 0 | 0 | NaN | 0 | NaN | 130 | Mexico | 1 | North America | Federal | Mexico city | 19.371887 | -99.086624 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 6 | Hostage Taking (Kidnapping) | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 45.0 | Diplomatic Personnel (outside of embassy, cons... | Belgian Ambassador Daughter | Nadine Chaval, daughter | 21.0 | Belgium | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23rd of September Communist League | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | 7.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 0.0 | NaN | NaN | NaN | Mexico | 1.0 | 800000.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | 0 | 1 | 1 | 1 | NaN |
| 2 | 197001000001 | 1970 | 1 | 0 | NaN | 0 | NaN | 160 | Philippines | 5 | Southeast Asia | Tarlac | Unknown | 15.478598 | 120.599741 | 4.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 1 | Assassination | NaN | NaN | NaN | NaN | 10 | Journalists & Media | 54.0 | Radio Journalist/Staff/Facility | Voice of America | Employee | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 3 | 197001000002 | 1970 | 1 | 0 | NaN | 0 | NaN | 78 | Greece | 8 | Western Europe | Attica | Athens | 37.997490 | 23.762728 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 46.0 | Embassy/Consulate | NaN | U.S. Embassy | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 16.0 | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Explosive | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| 4 | 197001000003 | 1970 | 1 | 0 | NaN | 0 | NaN | 101 | Japan | 4 | East Asia | Fukouka | Fukouka | 33.580412 | 130.396361 | 1.0 | 0 | NaN | NaN | 1 | 1 | 1 | -9.0 | NaN | NaN | 0.0 | 1 | 0 | 7 | Facility/Infrastructure Attack | NaN | NaN | NaN | NaN | 7 | Government (Diplomatic) | 46.0 | Embassy/Consulate | NaN | U.S. Consulate | 217.0 | United States | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8 | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Incendiary | NaN | NaN | NaN | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | PGIS | -9 | -9 | 1 | 1 | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 74202 | 201712310022 | 2017 | 12 | 31 | NaN | 0 | NaN | 182 | Somalia | 11 | Sub-Saharan Africa | Middle Shebelle | Ceelka Geelow | 2.359673 | 45.385034 | 2.0 | 0 | The incident occurred near the town of Balcad. | 12/31/2017: Assailants opened fire on a Somali... | 1 | 1 | 0 | 1.0 | 1.0 | Insurgency/Guerilla Action | 0.0 | 1 | 0 | 2 | Armed Assault | NaN | NaN | NaN | NaN | 4 | Military | 36.0 | Military Checkpoint | Somali National Army (SNA) | Checkpoint | 182.0 | Somalia | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Al-Shabaab | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 1.0 | 10.0 | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5 | Firearms | 5.0 | Unknown Gun Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | -9 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Somalia: Al-Shabaab Militants Attack Army Che... | "Highlights: Somalia Daily Media Highlights 2 ... | "Highlights: Somalia Daily Media Highlights 1 ... | START Primary Collection | 0 | 0 | 0 | 0 | NaN |
| 74203 | 201712310029 | 2017 | 12 | 31 | NaN | 0 | NaN | 200 | Syria | 10 | Middle East & North Africa | Lattakia | Jableh | 35.407278 | 35.942679 | 1.0 | 1 | The incident occurred at the Humaymim Airport. | 12/31/2017: Assailants launched mortars at the... | 1 | 1 | 0 | 1.0 | 1.0 | Insurgency/Guerilla Action | 0.0 | 1 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 4 | Military | 27.0 | Military Barracks/Base/Headquarters/Checkpost | Russian Air Force | Hmeymim Air Base | 167.0 | Russia | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Muslim extremists | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 11.0 | Projectile (rockets, mortars, RPGs, etc.) | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Mortars were used in the attack. | 2.0 | 0.0 | 0.0 | 7.0 | 0.0 | 0.0 | 1 | 4.0 | Unknown | -99.0 | Seven military planes were damaged in this att... | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Putin's 'victory' in Syria has turned into a ... | "Two Russian soldiers killed at Hmeymim base i... | "Two Russian servicemen killed in Syria mortar... | START Primary Collection | -9 | -9 | 1 | 1 | NaN |
| 74204 | 201712310030 | 2017 | 12 | 31 | NaN | 0 | NaN | 160 | Philippines | 5 | Southeast Asia | Maguindanao | Kubentog | 6.900742 | 124.437908 | 2.0 | 0 | The incident occurred in the Datu Hoffer distr... | 12/31/2017: Assailants set fire to houses in K... | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 1 | 0 | 7 | Facility/Infrastructure Attack | NaN | NaN | NaN | NaN | 14 | Private Citizens & Property | 76.0 | House/Apartment/Residence | Not Applicable | Houses | 160.0 | Philippines | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Bangsamoro Islamic Freedom Movement (BIFM) | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8 | Incendiary | 18.0 | Arson/Fire | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 4.0 | Unknown | -99.0 | Houses were damaged in this attack. | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Maguindanao clashes trap tribe members," Phil... | NaN | NaN | START Primary Collection | 0 | 0 | 0 | 0 | NaN |
| 74205 | 201712310031 | 2017 | 12 | 31 | NaN | 0 | NaN | 92 | India | 6 | South Asia | Manipur | Imphal | 24.798346 | 93.940430 | 1.0 | 0 | The incident occurred in the Mantripukhri neig... | 12/31/2017: Assailants threw a grenade at a Fo... | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 0 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 2 | Government (General) | 21.0 | Government Building/Facility/Office | Forest Department Manipur | Office | 92.0 | India | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 7.0 | Grenade | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | A thrown grenade was used in the attack. | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -9 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Trader escapes grenade attack in Imphal," Bus... | NaN | NaN | START Primary Collection | -9 | -9 | 0 | -9 | NaN |
| 74206 | 201712310032 | 2017 | 12 | 31 | NaN | 0 | NaN | 160 | Philippines | 5 | Southeast Asia | Maguindanao | Cotabato City | 7.209594 | 124.241966 | 1.0 | 0 | NaN | 12/31/2017: An explosive device was discovered... | 1 | 1 | 1 | 0.0 | NaN | NaN | 0.0 | 0 | 0 | 3 | Bombing/Explosion | NaN | NaN | NaN | NaN | 20 | Unknown | NaN | NaN | Unknown | Unknown | 160.0 | Philippines | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | Unknown | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | 0 | -99.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6 | Explosives | 16.0 | Unknown Explosive Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | An explosive device containing a detonating co... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | "Security tightened in Cotabato following IED ... | "Security tightened in Cotabato City," Manila ... | NaN | START Primary Collection | -9 | -9 | 0 | -9 | NaN |
181691 rows × 135 columns
df_dd["country_txt"].value_counts()
country_txt Iraq 24636 Pakistan 14368 Afghanistan 12731 India 11960 Colombia 8306 Philippines 6908 Peru 6096 El Salvador 5320 United Kingdom 5235 Turkey 4292 Somalia 4142 Nigeria 3907 Thailand 3849 Yemen 3347 Spain 3249 Sri Lanka 3022 United States 2836 Algeria 2743 France 2693 Egypt 2479 Lebanon 2478 Chile 2365 Libya 2249 West Bank and Gaza Strip 2227 Syria 2201 Russia 2194 Israel 2183 Guatemala 2050 South Africa 2016 Nicaragua 1970 Ukraine 1709 Bangladesh 1648 Italy 1565 Greece 1275 Nepal 1215 Sudan 967 Argentina 815 Democratic Republic of the Congo 775 Indonesia 761 Germany 735 Iran 684 Kenya 683 Burundi 613 Mali 566 Myanmar 546 West Germany (FRG) 541 Mexico 524 Angola 499 Japan 402 Uganda 394 Saudi Arabia 371 Mozambique 363 Cameroon 332 Honduras 323 Bolivia 314 Ireland 307 Venezuela 293 Central African Republic 283 Brazil 273 Cambodia 259 China 252 South Sudan 225 Ecuador 220 Georgia 217 Haiti 213 Bahrain 207 Yugoslavia 203 Kosovo 196 Ethiopia 190 Tajikistan 188 Bosnia-Herzegovina 159 Rwanda 159 Niger 154 Belgium 154 Namibia 151 Portugal 140 Sweden 132 Cyprus 132 Netherlands 130 Panama 127 Senegal 118 Macedonia 118 Austria 115 Australia 114 Paraguay 114 Jordan 113 Switzerland 111 Tunisia 109 Zimbabwe 101 Sierra Leone 101 Malaysia 99 Canada 96 Chad 91 Dominican Republic 90 Papua New Guinea 89 Rhodesia 83 Uruguay 82 Albania 80 Soviet Union 78 Kuwait 76 Ivory Coast 74 Costa Rica 67 Suriname 66 Zambia 62 Tanzania 59 Croatia 57 Guadeloupe 56 Bulgaria 52 Burkina Faso 52 Zaire 50 Taiwan 50 Azerbaijan 49 Togo 48 Hungary 46 Denmark 41 Poland 39 South Korea 38 East Germany (GDR) 38 Morocco 36 Jamaica 36 Republic of the Congo 36 Kyrgyzstan 35 Liberia 34 Macau 33 Czech Republic 32 New Caledonia 31 Cuba 30 Lesotho 29 Kazakhstan 27 Madagascar 27 Laos 27 Guyana 26 Hong Kong 26 Guinea 25 Armenia 24 Malta 23 Maldives 22 United Arab Emirates 22 Trinidad and Tobago 22 Djibouti 22 Uzbekistan 21 Moldova 21 Finland 20 New Zealand 20 Ghana 19 Norway 19 Mauritania 18 Slovak Republic 18 Fiji 17 Latvia 17 Swaziland 16 Estonia 16 Luxembourg 16 Belarus 13 Vietnam 12 Martinique 12 Serbia 12 Serbia-Montenegro 11 East Timor 10 Botswana 10 Eritrea 10 Czechoslovakia 10 Guinea-Bissau 9 Gabon 8 Lithuania 8 Benin 8 Belize 8 Qatar 7 Singapore 7 French Guiana 7 Romania 6 Bhutan 6 North Yemen 6 Slovenia 6 Brunei 6 Grenada 5 Bahamas 5 Montenegro 5 Western Sahara 5 Comoros 5 Malawi 5 People's Republic of the Congo 4 Iceland 4 Solomon Islands 4 Gambia 3 Dominica 3 French Polynesia 3 Barbados 3 Vanuatu 2 Turkmenistan 2 Seychelles 2 Mauritius 2 St. Kitts and Nevis 2 Equatorial Guinea 2 South Yemen 2 Vatican City 1 Falkland Islands 1 St. Lucia 1 North Korea 1 New Hebrides 1 International 1 Wallis and Futuna 1 South Vietnam 1 Andorra 1 Antigua and Barbuda 1 Name: count, dtype: int64
df_dd.groupby("country_txt").count().sort_values(by="nkill",ascending=False)
| eventid | iyear | imonth | iday | approxdate | extended | resolution | country | region | region_txt | provstate | city | latitude | longitude | specificity | vicinity | location | summary | crit1 | crit2 | crit3 | doubtterr | alternative | alternative_txt | multiple | success | suicide | attacktype1 | attacktype1_txt | attacktype2 | attacktype2_txt | attacktype3 | attacktype3_txt | targtype1 | targtype1_txt | targsubtype1 | targsubtype1_txt | corp1 | target1 | natlty1 | natlty1_txt | targtype2 | targtype2_txt | targsubtype2 | targsubtype2_txt | corp2 | target2 | natlty2 | natlty2_txt | targtype3 | targtype3_txt | targsubtype3 | targsubtype3_txt | corp3 | target3 | natlty3 | natlty3_txt | gname | gsubname | gname2 | gsubname2 | gname3 | gsubname3 | motive | guncertain1 | guncertain2 | guncertain3 | individual | nperps | nperpcap | claimed | claimmode | claimmode_txt | claim2 | claimmode2 | claimmode2_txt | claim3 | claimmode3 | claimmode3_txt | compclaim | weaptype1 | weaptype1_txt | weapsubtype1 | weapsubtype1_txt | weaptype2 | weaptype2_txt | weapsubtype2 | weapsubtype2_txt | weaptype3 | weaptype3_txt | weapsubtype3 | weapsubtype3_txt | weaptype4 | weaptype4_txt | weapsubtype4 | weapsubtype4_txt | weapdetail | nkill | nkillus | nkillter | nwound | nwoundus | nwoundte | property | propextent | propextent_txt | propvalue | propcomment | ishostkid | nhostkid | nhostkidus | nhours | ndays | divert | kidhijcountry | ransom | ransomamt | ransomamtus | ransompaid | ransompaidus | ransomnote | hostkidoutcome | hostkidoutcome_txt | nreleased | addnotes | scite1 | scite2 | scite3 | dbsource | INT_LOG | INT_IDEO | INT_MISC | INT_ANY | related | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| country_txt | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Iraq | 24636 | 24636 | 24636 | 24636 | 4558 | 24636 | 83 | 24636 | 24636 | 24636 | 24636 | 24620 | 24487 | 24487 | 24636 | 24636 | 14085 | 24494 | 24636 | 24636 | 24636 | 24636 | 2922 | 2922 | 24636 | 24636 | 24636 | 24636 | 24636 | 498 | 498 | 23 | 23 | 24636 | 24636 | 22953 | 22953 | 22090 | 24592 | 24607 | 24607 | 1925 | 1925 | 1881 | 1881 | 1847 | 1914 | 1869 | 1869 | 165 | 165 | 156 | 156 | 155 | 166 | 161 | 161 | 24636 | 553 | 98 | 11 | 4 | 0 | 9487 | 24635 | 97 | 4 | 24636 | 21923 | 23753 | 24494 | 2649 | 2649 | 99 | 17 | 17 | 4 | 2 | 2 | 586 | 24636 | 24636 | 23387 | 23387 | 961 | 961 | 743 | 743 | 133 | 133 | 114 | 114 | 0 | 0 | 0 | 0 | 11203 | 23911 | 24323 | 23925 | 23370 | 24253 | 23658 | 24636 | 8969 | 8969 | 5234 | 12182 | 24636 | 1025 | 1020 | 282 | 756 | 18 | 60 | 1209 | 29 | 20 | 20 | 20 | 23 | 1017 | 1017 | 978 | 5014 | 24487 | 14118 | 7080 | 24636 | 24636 | 24636 | 24636 | 24636 | 3772 |
| Pakistan | 14368 | 14368 | 14368 | 14368 | 356 | 14368 | 107 | 14368 | 14368 | 14368 | 14368 | 14357 | 14318 | 14318 | 14367 | 14368 | 6835 | 12627 | 14368 | 14368 | 14368 | 14368 | 1824 | 1824 | 14368 | 14368 | 14368 | 14368 | 14368 | 440 | 440 | 28 | 28 | 14368 | 14368 | 13309 | 13309 | 12227 | 14348 | 14305 | 14305 | 793 | 793 | 767 | 767 | 700 | 788 | 771 | 771 | 55 | 55 | 55 | 55 | 49 | 57 | 56 | 56 | 14368 | 305 | 194 | 18 | 26 | 2 | 5060 | 14368 | 190 | 26 | 14368 | 12127 | 12487 | 12627 | 2029 | 2029 | 190 | 105 | 105 | 26 | 11 | 11 | 289 | 14368 | 14368 | 13421 | 13421 | 775 | 775 | 751 | 751 | 126 | 126 | 122 | 122 | 1 | 1 | 2 | 2 | 8114 | 14119 | 12642 | 12593 | 13899 | 12633 | 12488 | 14368 | 6126 | 6126 | 3823 | 6807 | 14368 | 899 | 897 | 172 | 695 | 15 | 81 | 2646 | 54 | 37 | 38 | 36 | 36 | 845 | 845 | 828 | 2459 | 12621 | 9038 | 5286 | 14368 | 14368 | 14368 | 14368 | 14368 | 1390 |
| Afghanistan | 12731 | 12731 | 12731 | 12731 | 1099 | 12731 | 102 | 12731 | 12731 | 12731 | 12731 | 12592 | 12639 | 12639 | 12730 | 12731 | 5417 | 12617 | 12731 | 12731 | 12731 | 12731 | 1955 | 1955 | 12731 | 12731 | 12731 | 12731 | 12731 | 629 | 629 | 25 | 25 | 12731 | 12731 | 11856 | 11856 | 11735 | 12715 | 11997 | 11997 | 1532 | 1532 | 1518 | 1518 | 1429 | 1530 | 1500 | 1500 | 142 | 142 | 140 | 140 | 136 | 145 | 141 | 141 | 12731 | 17 | 135 | 4 | 10 | 0 | 3586 | 12731 | 135 | 10 | 12731 | 11748 | 12406 | 12617 | 3847 | 3847 | 135 | 41 | 41 | 9 | 1 | 1 | 482 | 12731 | 12731 | 11048 | 11048 | 826 | 826 | 735 | 735 | 112 | 112 | 110 | 110 | 3 | 3 | 3 | 3 | 5549 | 12362 | 12561 | 12303 | 11994 | 12510 | 12006 | 12731 | 4809 | 4809 | 3382 | 5608 | 12731 | 1121 | 1115 | 265 | 869 | 7 | 36 | 1221 | 29 | 18 | 20 | 18 | 48 | 1118 | 1118 | 1097 | 3294 | 12607 | 8459 | 4571 | 12731 | 12731 | 12731 | 12731 | 12731 | 1662 |
| India | 11960 | 11960 | 11960 | 11960 | 196 | 11960 | 212 | 11960 | 11960 | 11960 | 11960 | 11960 | 11801 | 11801 | 11958 | 11960 | 5306 | 9097 | 11960 | 11960 | 11960 | 11960 | 1105 | 1105 | 11960 | 11960 | 11960 | 11960 | 11960 | 804 | 804 | 53 | 53 | 11960 | 11960 | 11113 | 11113 | 9736 | 11918 | 11934 | 11934 | 673 | 673 | 647 | 647 | 571 | 670 | 648 | 648 | 55 | 55 | 53 | 53 | 40 | 56 | 54 | 54 | 11960 | 815 | 257 | 15 | 48 | 1 | 5685 | 11956 | 244 | 47 | 11960 | 8178 | 8826 | 9097 | 1491 | 1491 | 253 | 66 | 66 | 46 | 21 | 21 | 487 | 11960 | 11960 | 10841 | 10841 | 1145 | 1145 | 1068 | 1068 | 163 | 163 | 153 | 153 | 2 | 2 | 2 | 2 | 7240 | 11740 | 9136 | 9073 | 11506 | 9126 | 8962 | 11960 | 3499 | 3499 | 1701 | 3570 | 11960 | 1366 | 1356 | 582 | 787 | 18 | 161 | 4089 | 113 | 98 | 100 | 97 | 61 | 1287 | 1287 | 1265 | 1856 | 9090 | 6515 | 3623 | 11960 | 11960 | 11960 | 11960 | 11960 | 1512 |
| Colombia | 8306 | 8306 | 8306 | 8306 | 182 | 8306 | 384 | 8306 | 8306 | 8306 | 8306 | 8306 | 7835 | 7835 | 8306 | 8306 | 1104 | 2313 | 8306 | 8306 | 8306 | 8306 | 1094 | 1094 | 8306 | 8306 | 8306 | 8306 | 8306 | 185 | 185 | 19 | 19 | 8306 | 8306 | 7855 | 7855 | 5638 | 8289 | 8280 | 8280 | 357 | 357 | 327 | 327 | 305 | 355 | 343 | 343 | 58 | 58 | 48 | 48 | 53 | 59 | 56 | 56 | 8306 | 543 | 104 | 8 | 1 | 0 | 1549 | 8306 | 103 | 1 | 8306 | 2610 | 2144 | 2314 | 173 | 173 | 71 | 2 | 2 | 0 | 0 | 0 | 135 | 8306 | 8306 | 6727 | 6727 | 446 | 446 | 395 | 395 | 58 | 58 | 54 | 54 | 11 | 11 | 9 | 9 | 6147 | 7848 | 2417 | 2385 | 7722 | 2408 | 2311 | 8306 | 2632 | 2632 | 1288 | 1290 | 8306 | 1298 | 1292 | 421 | 628 | 12 | 695 | 6365 | 180 | 48 | 87 | 50 | 35 | 718 | 718 | 640 | 902 | 2311 | 1547 | 703 | 8306 | 8306 | 8306 | 8306 | 8306 | 1129 |
| Philippines | 6908 | 6908 | 6908 | 6908 | 92 | 6908 | 147 | 6908 | 6908 | 6908 | 6908 | 6908 | 6528 | 6528 | 6908 | 6908 | 2097 | 4970 | 6908 | 6908 | 6908 | 6908 | 1572 | 1572 | 6908 | 6908 | 6908 | 6908 | 6908 | 340 | 340 | 27 | 27 | 6908 | 6908 | 6512 | 6512 | 5802 | 6901 | 6896 | 6896 | 456 | 456 | 440 | 440 | 415 | 458 | 448 | 448 | 59 | 59 | 55 | 55 | 56 | 60 | 57 | 57 | 6908 | 446 | 85 | 0 | 20 | 0 | 2354 | 6906 | 83 | 20 | 6908 | 5083 | 4844 | 4970 | 647 | 647 | 83 | 7 | 7 | 20 | 0 | 0 | 141 | 6908 | 6908 | 6279 | 6279 | 695 | 695 | 648 | 648 | 64 | 64 | 62 | 62 | 0 | 0 | 0 | 0 | 4180 | 6694 | 4996 | 4902 | 6605 | 4979 | 4797 | 6908 | 2160 | 2160 | 1325 | 2128 | 6907 | 724 | 723 | 270 | 432 | 3 | 183 | 2466 | 123 | 64 | 93 | 63 | 45 | 602 | 602 | 582 | 1040 | 4970 | 3403 | 1945 | 6908 | 6908 | 6908 | 6908 | 6908 | 740 |
| Peru | 6096 | 6096 | 6096 | 6096 | 5 | 6096 | 46 | 6096 | 6096 | 6096 | 6096 | 6096 | 5808 | 5808 | 6096 | 6096 | 33 | 83 | 6096 | 6096 | 6096 | 6096 | 505 | 505 | 6096 | 6096 | 6096 | 6096 | 6096 | 8 | 8 | 0 | 0 | 6096 | 6096 | 5878 | 5878 | 3079 | 6060 | 6074 | 6074 | 55 | 55 | 47 | 47 | 27 | 50 | 50 | 50 | 16 | 16 | 13 | 13 | 2 | 16 | 16 | 16 | 6096 | 5 | 22 | 0 | 0 | 0 | 46 | 6096 | 22 | 0 | 6096 | 977 | 78 | 83 | 13 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6096 | 6096 | 5410 | 5410 | 407 | 407 | 350 | 350 | 43 | 43 | 36 | 36 | 8 | 8 | 9 | 9 | 5732 | 5457 | 386 | 201 | 5392 | 380 | 175 | 6096 | 1624 | 1624 | 1107 | 278 | 6096 | 228 | 228 | 71 | 49 | 3 | 82 | 6025 | 17 | 2 | 5 | 1 | 1 | 73 | 73 | 47 | 20 | 83 | 40 | 21 | 6096 | 6096 | 6096 | 6096 | 6096 | 1182 |
| United Kingdom | 5235 | 5235 | 5235 | 5235 | 16 | 5235 | 13 | 5235 | 5235 | 5235 | 5235 | 5235 | 5227 | 5227 | 5235 | 5235 | 982 | 1213 | 5235 | 5235 | 5235 | 5235 | 1003 | 1003 | 5235 | 5235 | 5235 | 5235 | 5235 | 26 | 26 | 1 | 1 | 5235 | 5235 | 4922 | 4922 | 3279 | 5219 | 5216 | 5216 | 75 | 75 | 69 | 69 | 60 | 72 | 72 | 72 | 5 | 5 | 4 | 4 | 4 | 5 | 6 | 6 | 5235 | 39 | 10 | 0 | 1 | 0 | 681 | 5235 | 10 | 1 | 5235 | 1183 | 1143 | 1215 | 226 | 226 | 10 | 5 | 5 | 1 | 1 | 1 | 107 | 5235 | 5235 | 4661 | 4661 | 189 | 189 | 154 | 154 | 14 | 14 | 13 | 13 | 2 | 2 | 0 | 0 | 4448 | 5065 | 1311 | 1326 | 3521 | 1309 | 1263 | 5235 | 1091 | 1091 | 591 | 555 | 5235 | 82 | 82 | 26 | 8 | 1 | 53 | 4040 | 11 | 0 | 1 | 0 | 1 | 58 | 58 | 32 | 189 | 1207 | 959 | 620 | 5235 | 5235 | 5235 | 5235 | 5235 | 398 |
| Turkey | 4292 | 4292 | 4292 | 4292 | 90 | 4292 | 32 | 4292 | 4292 | 4292 | 4292 | 4292 | 4126 | 4126 | 4292 | 4292 | 588 | 1936 | 4292 | 4292 | 4292 | 4292 | 967 | 967 | 4292 | 4292 | 4292 | 4292 | 4292 | 91 | 91 | 2 | 2 | 4292 | 4292 | 4097 | 4097 | 3320 | 4283 | 4271 | 4271 | 241 | 241 | 233 | 233 | 220 | 237 | 237 | 237 | 25 | 25 | 24 | 24 | 23 | 23 | 25 | 25 | 4292 | 212 | 67 | 2 | 3 | 0 | 618 | 4292 | 67 | 3 | 4292 | 1950 | 1864 | 1936 | 216 | 216 | 66 | 8 | 8 | 3 | 1 | 1 | 84 | 4292 | 4292 | 3841 | 3841 | 357 | 357 | 309 | 309 | 36 | 36 | 29 | 29 | 0 | 0 | 0 | 0 | 3236 | 4160 | 2063 | 2072 | 4133 | 2057 | 2048 | 4292 | 1198 | 1198 | 851 | 904 | 4292 | 199 | 199 | 69 | 102 | 11 | 59 | 2481 | 12 | 2 | 2 | 2 | 3 | 153 | 153 | 145 | 500 | 1935 | 1377 | 846 | 4292 | 4292 | 4292 | 4292 | 4292 | 489 |
| El Salvador | 5320 | 5320 | 5320 | 5320 | 0 | 5320 | 46 | 5320 | 5320 | 5320 | 5303 | 5320 | 4846 | 4846 | 5320 | 5320 | 0 | 0 | 5320 | 5320 | 5320 | 5320 | 1622 | 1622 | 5320 | 5320 | 5320 | 5320 | 5320 | 4 | 4 | 0 | 0 | 5320 | 5320 | 5142 | 5142 | 2190 | 5281 | 5317 | 5317 | 18 | 18 | 11 | 11 | 14 | 15 | 17 | 17 | 2 | 2 | 1 | 1 | 1 | 2 | 2 | 2 | 5320 | 518 | 5 | 0 | 0 | 0 | 0 | 5320 | 5 | 0 | 5320 | 312 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5320 | 5320 | 4736 | 4736 | 385 | 385 | 300 | 300 | 61 | 61 | 53 | 53 | 11 | 11 | 11 | 11 | 5004 | 3939 | 25 | 57 | 3901 | 23 | 41 | 5320 | 3133 | 3133 | 1738 | 158 | 5320 | 369 | 369 | 120 | 48 | 0 | 118 | 5320 | 27 | 0 | 2 | 0 | 0 | 70 | 70 | 45 | 0 | 0 | 0 | 0 | 5320 | 5320 | 5320 | 5320 | 5320 | 992 |
| Thailand | 3849 | 3849 | 3849 | 3849 | 20 | 3849 | 13 | 3849 | 3849 | 3849 | 3830 | 3612 | 3806 | 3806 | 3848 | 3849 | 1818 | 3632 | 3849 | 3849 | 3849 | 3849 | 601 | 601 | 3849 | 3849 | 3849 | 3849 | 3849 | 141 | 141 | 2 | 2 | 3849 | 3849 | 3643 | 3643 | 3101 | 3843 | 3845 | 3845 | 419 | 419 | 417 | 417 | 382 | 419 | 407 | 407 | 37 | 37 | 35 | 35 | 30 | 37 | 31 | 31 | 3849 | 48 | 36 | 0 | 1 | 0 | 2071 | 3848 | 36 | 1 | 3849 | 3057 | 3439 | 3632 | 66 | 66 | 36 | 0 | 0 | 1 | 0 | 0 | 90 | 3849 | 3849 | 3775 | 3775 | 293 | 293 | 274 | 274 | 28 | 28 | 25 | 25 | 0 | 0 | 0 | 0 | 2473 | 3836 | 3636 | 3626 | 3808 | 3635 | 3603 | 3849 | 1833 | 1833 | 1111 | 1909 | 3849 | 44 | 44 | 29 | 7 | 2 | 11 | 255 | 3 | 0 | 0 | 0 | 2 | 34 | 34 | 30 | 770 | 3627 | 2755 | 1524 | 3849 | 3849 | 3849 | 3849 | 3849 | 925 |
| Nigeria | 3907 | 3907 | 3907 | 3907 | 154 | 3907 | 43 | 3907 | 3907 | 3907 | 3907 | 3907 | 3866 | 3866 | 3907 | 3907 | 1730 | 3847 | 3907 | 3907 | 3907 | 3907 | 390 | 390 | 3907 | 3907 | 3907 | 3907 | 3907 | 837 | 837 | 76 | 76 | 3907 | 3907 | 3773 | 3773 | 3737 | 3902 | 3898 | 3898 | 351 | 351 | 341 | 341 | 323 | 349 | 342 | 342 | 51 | 51 | 48 | 48 | 48 | 51 | 49 | 49 | 3907 | 40 | 36 | 0 | 0 | 0 | 1002 | 3907 | 35 | 0 | 3907 | 3712 | 3803 | 3847 | 401 | 401 | 36 | 3 | 3 | 1 | 1 | 1 | 80 | 3907 | 3907 | 3675 | 3675 | 1119 | 1119 | 1030 | 1030 | 261 | 261 | 253 | 253 | 0 | 0 | 0 | 0 | 1254 | 3615 | 3843 | 3710 | 2916 | 3840 | 3580 | 3907 | 1907 | 1907 | 1707 | 2212 | 3907 | 510 | 506 | 120 | 400 | 7 | 11 | 554 | 67 | 53 | 55 | 52 | 43 | 509 | 509 | 492 | 1339 | 3847 | 2970 | 2141 | 3907 | 3907 | 3907 | 3907 | 3907 | 1366 |
| Somalia | 4142 | 4142 | 4142 | 4142 | 323 | 4142 | 49 | 4142 | 4142 | 4142 | 4142 | 4142 | 4120 | 4120 | 4141 | 4142 | 1779 | 3978 | 4142 | 4142 | 4142 | 4142 | 1446 | 1446 | 4142 | 4142 | 4142 | 4142 | 4142 | 214 | 214 | 22 | 22 | 4142 | 4142 | 4016 | 4016 | 3833 | 4137 | 4072 | 4072 | 705 | 705 | 686 | 686 | 675 | 699 | 692 | 692 | 56 | 56 | 51 | 51 | 53 | 55 | 56 | 56 | 4142 | 16 | 36 | 0 | 0 | 0 | 1169 | 4142 | 16 | 0 | 4142 | 3806 | 3930 | 3978 | 1147 | 1147 | 37 | 16 | 16 | 0 | 0 | 0 | 70 | 4142 | 4142 | 3523 | 3523 | 425 | 425 | 401 | 401 | 48 | 48 | 45 | 45 | 0 | 0 | 0 | 0 | 1870 | 3449 | 3979 | 3508 | 3140 | 3977 | 3374 | 4142 | 1337 | 1337 | 1022 | 1988 | 4142 | 415 | 415 | 165 | 268 | 6 | 48 | 533 | 16 | 8 | 12 | 10 | 4 | 396 | 396 | 390 | 788 | 3976 | 2698 | 1391 | 4142 | 4142 | 4142 | 4142 | 4142 | 421 |
| Yemen | 3347 | 3347 | 3347 | 3347 | 590 | 3347 | 38 | 3347 | 3347 | 3347 | 3347 | 3347 | 3270 | 3270 | 3347 | 3347 | 1033 | 3264 | 3347 | 3347 | 3347 | 3347 | 995 | 995 | 3347 | 3347 | 3347 | 3347 | 3347 | 132 | 132 | 5 | 5 | 3347 | 3347 | 3218 | 3218 | 3258 | 3347 | 3340 | 3340 | 271 | 271 | 267 | 267 | 260 | 271 | 271 | 271 | 20 | 20 | 19 | 19 | 19 | 20 | 19 | 19 | 3347 | 83 | 51 | 0 | 0 | 0 | 717 | 3347 | 49 | 0 | 3347 | 3221 | 3246 | 3264 | 421 | 421 | 50 | 2 | 2 | 0 | 0 | 0 | 30 | 3347 | 3347 | 2702 | 2702 | 230 | 230 | 220 | 220 | 40 | 40 | 39 | 39 | 1 | 1 | 1 | 1 | 1192 | 3083 | 3261 | 3175 | 2935 | 3260 | 3092 | 3347 | 1081 | 1081 | 932 | 1649 | 3347 | 395 | 395 | 77 | 326 | 2 | 32 | 447 | 13 | 10 | 10 | 10 | 22 | 386 | 386 | 384 | 769 | 3264 | 1900 | 1141 | 3347 | 3347 | 3347 | 3347 | 3347 | 507 |
| Sri Lanka | 3022 | 3022 | 3022 | 3022 | 11 | 3022 | 24 | 3022 | 3022 | 3022 | 3022 | 3022 | 2909 | 2909 | 3022 | 3022 | 623 | 973 | 3022 | 3022 | 3022 | 3022 | 778 | 778 | 3022 | 3022 | 3022 | 3022 | 3022 | 80 | 80 | 1 | 1 | 3022 | 3022 | 2917 | 2917 | 2337 | 2994 | 3007 | 3007 | 172 | 172 | 158 | 158 | 117 | 159 | 168 | 168 | 22 | 22 | 22 | 22 | 17 | 22 | 22 | 22 | 3022 | 188 | 2 | 0 | 0 | 0 | 908 | 3022 | 2 | 0 | 3022 | 860 | 882 | 973 | 45 | 45 | 2 | 0 | 0 | 0 | 0 | 0 | 79 | 3022 | 3022 | 2604 | 2604 | 259 | 259 | 218 | 218 | 31 | 31 | 29 | 29 | 3 | 3 | 3 | 3 | 2200 | 2971 | 993 | 1004 | 2895 | 992 | 941 | 3022 | 546 | 546 | 221 | 376 | 3021 | 94 | 93 | 33 | 47 | 0 | 52 | 2095 | 8 | 3 | 3 | 2 | 2 | 58 | 58 | 37 | 392 | 973 | 746 | 432 | 3022 | 3022 | 3022 | 3022 | 3022 | 179 |
| Spain | 3249 | 3249 | 3249 | 3249 | 4 | 3249 | 41 | 3249 | 3249 | 3249 | 3249 | 3249 | 3195 | 3195 | 3249 | 3249 | 90 | 486 | 3249 | 3249 | 3249 | 3249 | 229 | 229 | 3249 | 3249 | 3249 | 3249 | 3249 | 21 | 21 | 0 | 0 | 3249 | 3249 | 3066 | 3066 | 1664 | 3222 | 3230 | 3230 | 65 | 65 | 53 | 53 | 53 | 62 | 58 | 58 | 18 | 18 | 16 | 16 | 14 | 17 | 18 | 18 | 3249 | 46 | 0 | 0 | 0 | 0 | 470 | 3249 | 0 | 0 | 3249 | 735 | 416 | 486 | 141 | 141 | 0 | 0 | 0 | 0 | 0 | 0 | 135 | 3249 | 3249 | 2847 | 2847 | 134 | 134 | 127 | 127 | 12 | 12 | 10 | 10 | 2 | 2 | 2 | 2 | 2829 | 2956 | 502 | 541 | 2967 | 501 | 535 | 3249 | 1305 | 1305 | 729 | 369 | 3249 | 123 | 123 | 42 | 36 | 5 | 99 | 2768 | 54 | 0 | 12 | 0 | 0 | 74 | 74 | 69 | 135 | 486 | 283 | 154 | 3249 | 3249 | 3249 | 3249 | 3249 | 307 |
| United States | 2836 | 2836 | 2836 | 2836 | 37 | 2836 | 12 | 2836 | 2836 | 2836 | 2832 | 2836 | 2835 | 2835 | 2836 | 2836 | 957 | 1783 | 2836 | 2836 | 2836 | 2836 | 410 | 410 | 2835 | 2836 | 2836 | 2836 | 2836 | 51 | 51 | 2 | 2 | 2836 | 2836 | 2716 | 2716 | 1898 | 2791 | 2827 | 2827 | 121 | 121 | 88 | 88 | 106 | 107 | 115 | 115 | 12 | 12 | 12 | 12 | 10 | 12 | 12 | 12 | 2836 | 44 | 48 | 21 | 8 | 0 | 1477 | 2836 | 48 | 8 | 2836 | 1854 | 1782 | 1786 | 523 | 523 | 42 | 24 | 24 | 7 | 7 | 7 | 131 | 2836 | 2836 | 2577 | 2577 | 148 | 148 | 108 | 108 | 31 | 31 | 22 | 22 | 0 | 0 | 0 | 0 | 2292 | 2763 | 1883 | 1833 | 2743 | 1859 | 1819 | 2836 | 1611 | 1611 | 961 | 1053 | 2660 | 66 | 66 | 42 | 22 | 7 | 14 | 2172 | 8 | 1 | 1 | 0 | 1 | 42 | 42 | 26 | 1238 | 1783 | 1617 | 1186 | 2836 | 2836 | 2836 | 2836 | 2836 | 478 |
| Algeria | 2743 | 2743 | 2743 | 2743 | 77 | 2743 | 53 | 2743 | 2743 | 2743 | 2706 | 2736 | 2617 | 2617 | 2743 | 2743 | 1016 | 1612 | 2743 | 2743 | 2743 | 2743 | 552 | 552 | 2743 | 2743 | 2743 | 2743 | 2743 | 192 | 192 | 7 | 7 | 2743 | 2743 | 2587 | 2587 | 1996 | 2708 | 2711 | 2711 | 184 | 184 | 179 | 179 | 174 | 179 | 180 | 180 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 2743 | 122 | 6 | 1 | 0 | 0 | 1507 | 2382 | 6 | 0 | 2743 | 1308 | 1491 | 1612 | 91 | 91 | 7 | 1 | 1 | 0 | 0 | 0 | 73 | 2743 | 2743 | 2428 | 2428 | 252 | 252 | 236 | 236 | 35 | 35 | 34 | 34 | 0 | 0 | 0 | 0 | 1900 | 2719 | 1738 | 1584 | 2630 | 1738 | 1550 | 2743 | 702 | 702 | 64 | 587 | 2743 | 187 | 182 | 63 | 118 | 6 | 45 | 1843 | 41 | 15 | 36 | 7 | 17 | 141 | 141 | 118 | 737 | 1609 | 825 | 363 | 2743 | 2743 | 2743 | 2743 | 2743 | 144 |
| France | 2693 | 2693 | 2693 | 2693 | 18 | 2693 | 15 | 2693 | 2693 | 2693 | 2693 | 2693 | 2672 | 2672 | 2693 | 2693 | 122 | 505 | 2693 | 2693 | 2693 | 2693 | 102 | 102 | 2693 | 2693 | 2693 | 2693 | 2693 | 13 | 13 | 1 | 1 | 2693 | 2693 | 2434 | 2434 | 1142 | 2630 | 2617 | 2617 | 65 | 65 | 62 | 62 | 51 | 64 | 61 | 61 | 10 | 10 | 8 | 8 | 7 | 10 | 9 | 9 | 2693 | 72 | 5 | 0 | 0 | 0 | 411 | 2693 | 5 | 0 | 2693 | 423 | 460 | 505 | 223 | 223 | 3 | 2 | 2 | 0 | 0 | 0 | 94 | 2693 | 2693 | 2397 | 2397 | 68 | 68 | 56 | 56 | 12 | 12 | 9 | 9 | 4 | 4 | 4 | 4 | 2203 | 2514 | 511 | 526 | 2509 | 510 | 519 | 2693 | 1034 | 1034 | 504 | 386 | 2693 | 51 | 51 | 35 | 22 | 7 | 19 | 2211 | 7 | 2 | 5 | 2 | 3 | 35 | 35 | 33 | 132 | 505 | 346 | 174 | 2693 | 2693 | 2693 | 2693 | 2693 | 761 |
| Egypt | 2479 | 2479 | 2479 | 2479 | 230 | 2479 | 0 | 2479 | 2479 | 2479 | 2461 | 2478 | 2458 | 2458 | 2479 | 2479 | 627 | 2008 | 2479 | 2479 | 2479 | 2479 | 461 | 461 | 2479 | 2479 | 2479 | 2479 | 2479 | 60 | 60 | 1 | 1 | 2479 | 2479 | 2241 | 2241 | 2201 | 2474 | 2479 | 2479 | 171 | 171 | 170 | 170 | 169 | 170 | 171 | 171 | 13 | 13 | 12 | 12 | 11 | 12 | 12 | 12 | 2479 | 15 | 36 | 7 | 9 | 0 | 335 | 2479 | 36 | 9 | 2479 | 2074 | 2007 | 2008 | 440 | 440 | 35 | 20 | 20 | 9 | 4 | 4 | 21 | 2479 | 2479 | 2317 | 2317 | 163 | 163 | 157 | 157 | 22 | 22 | 22 | 22 | 0 | 0 | 0 | 0 | 1221 | 2446 | 2012 | 2009 | 2433 | 2012 | 1995 | 2479 | 671 | 671 | 645 | 873 | 2479 | 104 | 104 | 31 | 68 | 3 | 4 | 570 | 4 | 4 | 4 | 4 | 6 | 101 | 101 | 99 | 421 | 2008 | 1300 | 852 | 2479 | 2479 | 2479 | 2479 | 2479 | 418 |
| Lebanon | 2478 | 2478 | 2478 | 2478 | 15 | 2478 | 61 | 2478 | 2478 | 2478 | 2478 | 2478 | 2333 | 2333 | 2478 | 2478 | 303 | 680 | 2478 | 2478 | 2478 | 2478 | 785 | 785 | 2478 | 2478 | 2478 | 2478 | 2478 | 30 | 30 | 0 | 0 | 2478 | 2478 | 2372 | 2372 | 1508 | 2458 | 2457 | 2457 | 101 | 101 | 93 | 93 | 86 | 97 | 98 | 98 | 11 | 11 | 9 | 9 | 7 | 10 | 10 | 10 | 2478 | 141 | 30 | 12 | 1 | 0 | 366 | 2477 | 29 | 1 | 2478 | 726 | 639 | 680 | 121 | 121 | 24 | 12 | 12 | 1 | 1 | 1 | 61 | 2478 | 2478 | 2263 | 2263 | 166 | 166 | 162 | 162 | 19 | 19 | 18 | 18 | 3 | 3 | 3 | 3 | 1999 | 2272 | 775 | 738 | 2254 | 767 | 722 | 2478 | 694 | 694 | 366 | 436 | 2478 | 191 | 191 | 55 | 70 | 13 | 149 | 1845 | 23 | 4 | 5 | 4 | 3 | 114 | 114 | 102 | 177 | 680 | 509 | 298 | 2478 | 2478 | 2478 | 2478 | 2478 | 140 |
| Russia | 2194 | 2194 | 2194 | 2194 | 30 | 2194 | 51 | 2194 | 2194 | 2194 | 2180 | 2190 | 2191 | 2191 | 2194 | 2194 | 1044 | 1946 | 2194 | 2194 | 2194 | 2194 | 305 | 305 | 2194 | 2194 | 2194 | 2194 | 2194 | 132 | 132 | 5 | 5 | 2194 | 2194 | 2074 | 2074 | 1704 | 2187 | 2179 | 2179 | 151 | 151 | 148 | 148 | 128 | 153 | 145 | 145 | 12 | 12 | 11 | 11 | 7 | 12 | 12 | 12 | 2194 | 146 | 8 | 0 | 2 | 1 | 1562 | 2187 | 8 | 2 | 2194 | 1394 | 1787 | 1946 | 150 | 150 | 8 | 3 | 3 | 0 | 0 | 0 | 142 | 2194 | 2194 | 2110 | 2110 | 193 | 193 | 182 | 182 | 18 | 18 | 17 | 17 | 0 | 0 | 0 | 0 | 1424 | 2169 | 1946 | 1884 | 2122 | 1942 | 1860 | 2194 | 958 | 958 | 211 | 947 | 2194 | 104 | 104 | 49 | 54 | 4 | 55 | 323 | 15 | 3 | 6 | 4 | 3 | 77 | 77 | 75 | 379 | 1942 | 1507 | 783 | 2194 | 2194 | 2194 | 2194 | 2194 | 172 |
| West Bank and Gaza Strip | 2227 | 2227 | 2227 | 2227 | 30 | 2227 | 31 | 2227 | 2227 | 2227 | 2227 | 2227 | 2215 | 2215 | 2227 | 2227 | 581 | 1310 | 2227 | 2227 | 2227 | 2227 | 604 | 604 | 2227 | 2227 | 2227 | 2227 | 2227 | 59 | 59 | 3 | 3 | 2227 | 2227 | 2111 | 2111 | 1764 | 2226 | 2168 | 2168 | 90 | 90 | 87 | 87 | 78 | 90 | 89 | 89 | 7 | 7 | 7 | 7 | 6 | 7 | 6 | 6 | 2227 | 72 | 46 | 6 | 11 | 1 | 699 | 2227 | 46 | 11 | 2227 | 1102 | 1211 | 1310 | 298 | 298 | 45 | 30 | 30 | 11 | 11 | 11 | 230 | 2227 | 2227 | 1997 | 1997 | 147 | 147 | 115 | 115 | 17 | 17 | 14 | 14 | 0 | 0 | 0 | 0 | 1435 | 2142 | 1329 | 1333 | 2129 | 1327 | 1318 | 2227 | 563 | 563 | 267 | 403 | 2227 | 83 | 80 | 45 | 47 | 4 | 34 | 973 | 1 | 1 | 2 | 1 | 3 | 75 | 75 | 64 | 291 | 1309 | 982 | 638 | 2227 | 2227 | 2227 | 2227 | 2227 | 104 |
| Israel | 2183 | 2183 | 2183 | 2183 | 10 | 2183 | 12 | 2183 | 2183 | 2183 | 2182 | 2183 | 2129 | 2129 | 2183 | 2183 | 314 | 1212 | 2183 | 2183 | 2183 | 2183 | 263 | 263 | 2183 | 2183 | 2183 | 2183 | 2183 | 18 | 18 | 0 | 0 | 2183 | 2183 | 1982 | 1982 | 1357 | 2173 | 2144 | 2144 | 84 | 84 | 80 | 80 | 73 | 83 | 81 | 81 | 5 | 5 | 4 | 4 | 4 | 4 | 6 | 6 | 2183 | 132 | 83 | 14 | 14 | 2 | 721 | 2183 | 82 | 13 | 2183 | 1056 | 1141 | 1212 | 506 | 506 | 77 | 70 | 70 | 14 | 14 | 14 | 342 | 2183 | 2183 | 2010 | 2010 | 91 | 91 | 82 | 82 | 11 | 11 | 10 | 10 | 1 | 1 | 1 | 1 | 1695 | 2029 | 1218 | 1226 | 2018 | 1214 | 1213 | 2183 | 762 | 762 | 344 | 507 | 2183 | 36 | 36 | 14 | 11 | 4 | 23 | 993 | 1 | 0 | 0 | 0 | 1 | 21 | 21 | 12 | 248 | 1211 | 820 | 479 | 2183 | 2183 | 2183 | 2183 | 2183 | 184 |
| Libya | 2249 | 2249 | 2249 | 2249 | 251 | 2249 | 0 | 2249 | 2249 | 2249 | 2249 | 2248 | 2222 | 2222 | 2249 | 2249 | 738 | 2235 | 2249 | 2249 | 2249 | 2249 | 495 | 495 | 2249 | 2249 | 2249 | 2249 | 2249 | 53 | 53 | 3 | 3 | 2249 | 2249 | 2151 | 2151 | 2249 | 2249 | 2249 | 2249 | 162 | 162 | 160 | 160 | 162 | 162 | 161 | 161 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 29 | 2249 | 42 | 43 | 3 | 8 | 0 | 416 | 2249 | 43 | 8 | 2249 | 2237 | 2235 | 2235 | 253 | 253 | 43 | 3 | 3 | 8 | 0 | 0 | 4 | 2249 | 2249 | 1858 | 1858 | 115 | 115 | 100 | 100 | 21 | 21 | 19 | 19 | 0 | 0 | 0 | 0 | 688 | 1994 | 2236 | 2194 | 1936 | 2236 | 2184 | 2249 | 778 | 778 | 777 | 1015 | 2249 | 451 | 451 | 74 | 378 | 3 | 4 | 466 | 27 | 26 | 26 | 26 | 23 | 453 | 453 | 453 | 416 | 2235 | 1187 | 695 | 2249 | 2249 | 2249 | 2249 | 2249 | 327 |
| Chile | 2365 | 2365 | 2365 | 2365 | 4 | 2365 | 13 | 2365 | 2365 | 2365 | 2365 | 2365 | 2320 | 2320 | 2365 | 2365 | 42 | 99 | 2365 | 2365 | 2365 | 2365 | 63 | 63 | 2365 | 2365 | 2365 | 2365 | 2365 | 7 | 7 | 0 | 0 | 2365 | 2365 | 2276 | 2276 | 1003 | 2361 | 2360 | 2360 | 22 | 22 | 16 | 16 | 13 | 21 | 21 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2365 | 116 | 6 | 0 | 0 | 0 | 73 | 2365 | 6 | 0 | 2365 | 285 | 95 | 99 | 40 | 40 | 2 | 2 | 2 | 0 | 0 | 0 | 10 | 2365 | 2365 | 2087 | 2087 | 138 | 138 | 81 | 81 | 9 | 9 | 8 | 8 | 1 | 1 | 1 | 1 | 2241 | 1952 | 112 | 133 | 1952 | 115 | 132 | 2365 | 832 | 832 | 568 | 356 | 2365 | 56 | 56 | 11 | 6 | 1 | 20 | 2269 | 3 | 0 | 1 | 0 | 0 | 16 | 16 | 12 | 28 | 99 | 69 | 34 | 2365 | 2365 | 2365 | 2365 | 2365 | 393 |
| South Africa | 2016 | 2016 | 2016 | 2016 | 4 | 2016 | 1 | 2016 | 2016 | 2016 | 2016 | 2016 | 1939 | 1939 | 2016 | 2016 | 64 | 154 | 2016 | 2016 | 2016 | 2016 | 242 | 242 | 2016 | 2016 | 2016 | 2016 | 2016 | 4 | 4 | 0 | 0 | 2016 | 2016 | 1958 | 1958 | 915 | 2014 | 2007 | 2007 | 14 | 14 | 10 | 10 | 10 | 12 | 14 | 14 | 3 | 3 | 1 | 1 | 2 | 2 | 3 | 3 | 2016 | 32 | 1 | 0 | 0 | 0 | 88 | 2016 | 1 | 0 | 2016 | 220 | 144 | 154 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2016 | 2016 | 1745 | 1745 | 116 | 116 | 101 | 101 | 10 | 10 | 7 | 7 | 1 | 1 | 1 | 1 | 1769 | 1939 | 474 | 485 | 1931 | 473 | 473 | 2016 | 195 | 195 | 138 | 140 | 2016 | 6 | 6 | 1 | 2 | 1 | 6 | 1863 | 1 | 0 | 0 | 0 | 0 | 6 | 6 | 3 | 38 | 154 | 122 | 77 | 2016 | 2016 | 2016 | 2016 | 2016 | 85 |
| Syria | 2201 | 2201 | 2201 | 2201 | 170 | 2201 | 3 | 2201 | 2201 | 2201 | 2201 | 2197 | 2166 | 2166 | 2201 | 2201 | 1009 | 2056 | 2201 | 2201 | 2201 | 2201 | 547 | 547 | 2201 | 2201 | 2201 | 2201 | 2201 | 97 | 97 | 9 | 9 | 2201 | 2201 | 2141 | 2141 | 2122 | 2196 | 2182 | 2182 | 293 | 293 | 290 | 290 | 291 | 293 | 293 | 293 | 27 | 27 | 27 | 27 | 27 | 27 | 27 | 27 | 2201 | 136 | 213 | 25 | 93 | 12 | 316 | 2201 | 213 | 93 | 2201 | 2064 | 2056 | 2056 | 709 | 709 | 213 | 81 | 81 | 93 | 28 | 28 | 83 | 2201 | 2201 | 1888 | 1888 | 258 | 258 | 177 | 177 | 40 | 40 | 32 | 32 | 0 | 0 | 0 | 0 | 1216 | 1931 | 2058 | 1936 | 1608 | 2058 | 1825 | 2201 | 819 | 819 | 774 | 1216 | 2201 | 256 | 256 | 53 | 201 | 3 | 5 | 397 | 21 | 20 | 20 | 20 | 17 | 254 | 254 | 253 | 944 | 2056 | 1669 | 1228 | 2201 | 2201 | 2201 | 2201 | 2201 | 751 |
| Guatemala | 2050 | 2050 | 2050 | 2050 | 0 | 2050 | 66 | 2050 | 2050 | 2050 | 2025 | 2050 | 1957 | 1957 | 2050 | 2050 | 7 | 20 | 2050 | 2050 | 2050 | 2050 | 519 | 519 | 2050 | 2050 | 2050 | 2050 | 2050 | 0 | 0 | 0 | 0 | 2050 | 2050 | 1946 | 1946 | 1076 | 2039 | 2039 | 2039 | 20 | 20 | 16 | 16 | 17 | 19 | 19 | 19 | 4 | 4 | 3 | 3 | 2 | 4 | 4 | 4 | 2050 | 16 | 0 | 0 | 0 | 0 | 14 | 2050 | 0 | 0 | 2050 | 169 | 20 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2050 | 2050 | 1593 | 1593 | 118 | 118 | 96 | 96 | 23 | 23 | 19 | 19 | 3 | 3 | 3 | 3 | 1714 | 1722 | 54 | 71 | 1699 | 55 | 59 | 2050 | 716 | 716 | 249 | 64 | 2050 | 269 | 269 | 61 | 44 | 0 | 205 | 2030 | 49 | 0 | 2 | 1 | 0 | 88 | 88 | 70 | 8 | 20 | 12 | 6 | 2050 | 2050 | 2050 | 2050 | 2050 | 106 |
| Bangladesh | 1648 | 1648 | 1648 | 1648 | 26 | 1648 | 12 | 1648 | 1648 | 1648 | 1648 | 1648 | 1578 | 1578 | 1648 | 1648 | 602 | 1093 | 1648 | 1648 | 1648 | 1648 | 108 | 108 | 1648 | 1648 | 1648 | 1648 | 1648 | 55 | 55 | 2 | 2 | 1648 | 1648 | 1495 | 1495 | 1498 | 1647 | 1639 | 1639 | 60 | 60 | 55 | 55 | 58 | 60 | 60 | 60 | 6 | 6 | 5 | 5 | 6 | 6 | 6 | 6 | 1648 | 194 | 43 | 3 | 12 | 1 | 696 | 1648 | 43 | 12 | 1648 | 1012 | 1055 | 1093 | 79 | 79 | 42 | 12 | 12 | 12 | 0 | 0 | 39 | 1648 | 1648 | 1424 | 1424 | 134 | 134 | 122 | 122 | 25 | 25 | 20 | 20 | 1 | 1 | 1 | 1 | 1159 | 1622 | 1089 | 1089 | 1607 | 1089 | 1081 | 1648 | 461 | 461 | 358 | 662 | 1648 | 62 | 62 | 25 | 36 | 0 | 14 | 598 | 6 | 4 | 6 | 4 | 4 | 50 | 50 | 44 | 205 | 1093 | 573 | 263 | 1648 | 1648 | 1648 | 1648 | 1648 | 323 |
| Ukraine | 1709 | 1709 | 1709 | 1709 | 211 | 1709 | 1 | 1709 | 1709 | 1709 | 1706 | 1709 | 1703 | 1703 | 1709 | 1709 | 281 | 1689 | 1709 | 1709 | 1709 | 1709 | 863 | 863 | 1709 | 1709 | 1709 | 1709 | 1709 | 16 | 16 | 0 | 0 | 1709 | 1709 | 1668 | 1668 | 1700 | 1709 | 1708 | 1708 | 83 | 83 | 82 | 82 | 83 | 83 | 83 | 83 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 1709 | 22 | 0 | 0 | 0 | 0 | 110 | 1709 | 0 | 0 | 1709 | 1686 | 1687 | 1689 | 104 | 104 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1709 | 1709 | 1548 | 1548 | 241 | 241 | 233 | 233 | 30 | 30 | 28 | 28 | 0 | 0 | 0 | 0 | 957 | 1610 | 1689 | 1657 | 1580 | 1689 | 1631 | 1709 | 571 | 571 | 559 | 1146 | 1709 | 129 | 129 | 21 | 109 | 2 | 2 | 148 | 2 | 2 | 2 | 2 | 9 | 128 | 128 | 128 | 399 | 1689 | 888 | 407 | 1709 | 1709 | 1709 | 1709 | 1709 | 382 |
| Italy | 1565 | 1565 | 1565 | 1565 | 1 | 1565 | 29 | 1565 | 1565 | 1565 | 1565 | 1565 | 1553 | 1553 | 1565 | 1565 | 38 | 137 | 1565 | 1565 | 1565 | 1565 | 57 | 57 | 1565 | 1565 | 1565 | 1565 | 1565 | 2 | 2 | 0 | 0 | 1565 | 1565 | 1458 | 1458 | 584 | 1560 | 1562 | 1562 | 23 | 23 | 18 | 18 | 14 | 22 | 21 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1565 | 76 | 5 | 0 | 1 | 0 | 107 | 1565 | 4 | 1 | 1565 | 385 | 131 | 137 | 56 | 56 | 4 | 3 | 3 | 1 | 1 | 1 | 30 | 1565 | 1565 | 1059 | 1059 | 92 | 92 | 74 | 74 | 4 | 4 | 2 | 2 | 1 | 1 | 1 | 1 | 1424 | 1461 | 160 | 157 | 1465 | 161 | 154 | 1565 | 774 | 774 | 564 | 111 | 1565 | 93 | 93 | 30 | 26 | 5 | 67 | 1430 | 19 | 0 | 6 | 0 | 0 | 53 | 53 | 49 | 33 | 136 | 105 | 71 | 1565 | 1565 | 1565 | 1565 | 1565 | 88 |
| Nicaragua | 1970 | 1970 | 1970 | 1970 | 0 | 1970 | 21 | 1970 | 1970 | 1970 | 1952 | 1970 | 1566 | 1566 | 1970 | 1970 | 2 | 4 | 1970 | 1970 | 1970 | 1970 | 987 | 987 | 1970 | 1970 | 1970 | 1970 | 1970 | 0 | 0 | 0 | 0 | 1970 | 1970 | 1912 | 1912 | 720 | 1954 | 1968 | 1968 | 8 | 8 | 8 | 8 | 8 | 8 | 7 | 7 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 1970 | 25 | 7 | 0 | 0 | 0 | 2 | 1970 | 7 | 0 | 1970 | 223 | 4 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1970 | 1970 | 1745 | 1745 | 129 | 129 | 100 | 100 | 8 | 8 | 6 | 6 | 2 | 2 | 2 | 2 | 1786 | 1422 | 16 | 30 | 1344 | 18 | 20 | 1970 | 1199 | 1199 | 413 | 48 | 1970 | 136 | 136 | 44 | 20 | 2 | 62 | 1967 | 25 | 1 | 7 | 1 | 0 | 35 | 35 | 30 | 2 | 4 | 3 | 3 | 1970 | 1970 | 1970 | 1970 | 1970 | 84 |
| Greece | 1275 | 1275 | 1275 | 1275 | 7 | 1275 | 7 | 1275 | 1275 | 1275 | 1275 | 1275 | 1269 | 1269 | 1275 | 1275 | 339 | 611 | 1275 | 1275 | 1275 | 1275 | 68 | 68 | 1275 | 1275 | 1275 | 1275 | 1275 | 15 | 15 | 0 | 0 | 1275 | 1275 | 1229 | 1229 | 825 | 1272 | 1270 | 1270 | 39 | 39 | 32 | 32 | 27 | 37 | 36 | 36 | 6 | 6 | 4 | 4 | 3 | 6 | 6 | 6 | 1275 | 12 | 33 | 1 | 4 | 0 | 473 | 1275 | 29 | 3 | 1275 | 502 | 565 | 611 | 256 | 256 | 25 | 16 | 16 | 4 | 2 | 2 | 112 | 1275 | 1275 | 1123 | 1123 | 55 | 55 | 47 | 47 | 6 | 6 | 4 | 4 | 0 | 0 | 0 | 0 | 1116 | 1263 | 646 | 618 | 1247 | 645 | 612 | 1275 | 655 | 655 | 310 | 504 | 1275 | 15 | 15 | 7 | 6 | 6 | 9 | 668 | 2 | 0 | 1 | 0 | 0 | 9 | 9 | 6 | 82 | 611 | 460 | 207 | 1275 | 1275 | 1275 | 1275 | 1275 | 242 |
| Nepal | 1215 | 1215 | 1215 | 1215 | 29 | 1215 | 21 | 1215 | 1215 | 1215 | 1215 | 1215 | 1206 | 1206 | 1215 | 1215 | 285 | 1180 | 1215 | 1215 | 1215 | 1215 | 63 | 63 | 1215 | 1215 | 1215 | 1215 | 1215 | 73 | 73 | 2 | 2 | 1215 | 1215 | 1084 | 1084 | 1094 | 1206 | 1214 | 1214 | 93 | 93 | 91 | 91 | 90 | 93 | 89 | 89 | 17 | 17 | 17 | 17 | 17 | 17 | 16 | 16 | 1215 | 15 | 22 | 0 | 9 | 0 | 915 | 1214 | 16 | 8 | 1215 | 888 | 1115 | 1180 | 282 | 282 | 22 | 18 | 18 | 9 | 9 | 9 | 171 | 1215 | 1215 | 1066 | 1066 | 80 | 80 | 70 | 70 | 8 | 8 | 7 | 7 | 1 | 1 | 1 | 1 | 562 | 1195 | 1180 | 1153 | 1153 | 1178 | 1136 | 1215 | 494 | 494 | 220 | 535 | 1215 | 129 | 125 | 18 | 95 | 6 | 20 | 153 | 6 | 2 | 4 | 1 | 4 | 128 | 128 | 81 | 252 | 1178 | 669 | 273 | 1215 | 1215 | 1215 | 1215 | 1215 | 144 |
| Sudan | 967 | 967 | 967 | 967 | 43 | 967 | 25 | 967 | 967 | 967 | 967 | 967 | 932 | 932 | 967 | 967 | 346 | 896 | 967 | 967 | 967 | 967 | 85 | 85 | 967 | 967 | 967 | 967 | 967 | 175 | 175 | 24 | 24 | 967 | 967 | 949 | 949 | 881 | 967 | 966 | 966 | 86 | 86 | 80 | 80 | 69 | 85 | 81 | 81 | 14 | 14 | 13 | 13 | 9 | 14 | 13 | 13 | 967 | 16 | 9 | 0 | 0 | 0 | 328 | 967 | 8 | 0 | 967 | 840 | 881 | 896 | 84 | 84 | 9 | 1 | 1 | 0 | 0 | 0 | 15 | 967 | 967 | 764 | 764 | 144 | 144 | 113 | 113 | 8 | 8 | 2 | 2 | 0 | 0 | 0 | 0 | 306 | 806 | 897 | 873 | 737 | 896 | 855 | 967 | 422 | 422 | 330 | 483 | 967 | 302 | 302 | 64 | 243 | 8 | 19 | 356 | 36 | 33 | 33 | 33 | 18 | 294 | 294 | 294 | 251 | 896 | 371 | 178 | 967 | 967 | 967 | 967 | 967 | 170 |
| Indonesia | 761 | 761 | 761 | 761 | 7 | 761 | 9 | 761 | 761 | 761 | 753 | 752 | 744 | 744 | 761 | 761 | 210 | 612 | 761 | 761 | 761 | 760 | 80 | 80 | 761 | 761 | 761 | 761 | 761 | 28 | 28 | 3 | 3 | 761 | 761 | 727 | 727 | 702 | 760 | 756 | 756 | 99 | 99 | 98 | 98 | 98 | 99 | 96 | 96 | 34 | 34 | 33 | 33 | 33 | 34 | 33 | 33 | 761 | 18 | 14 | 0 | 0 | 0 | 489 | 761 | 14 | 0 | 761 | 419 | 577 | 612 | 52 | 52 | 14 | 2 | 2 | 0 | 0 | 0 | 51 | 761 | 761 | 635 | 635 | 73 | 73 | 61 | 61 | 14 | 14 | 10 | 10 | 0 | 0 | 0 | 0 | 315 | 754 | 613 | 606 | 729 | 609 | 590 | 761 | 295 | 295 | 92 | 234 | 761 | 46 | 43 | 8 | 29 | 1 | 9 | 181 | 7 | 4 | 5 | 4 | 5 | 38 | 38 | 33 | 209 | 612 | 400 | 245 | 761 | 761 | 761 | 761 | 761 | 200 |
| Germany | 735 | 735 | 735 | 735 | 10 | 735 | 7 | 735 | 735 | 735 | 735 | 735 | 713 | 713 | 735 | 735 | 53 | 225 | 735 | 735 | 735 | 735 | 32 | 32 | 735 | 735 | 735 | 735 | 735 | 2 | 2 | 0 | 0 | 735 | 735 | 674 | 674 | 485 | 734 | 705 | 705 | 13 | 13 | 10 | 10 | 11 | 13 | 12 | 12 | 3 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 735 | 2 | 2 | 0 | 0 | 0 | 141 | 735 | 2 | 0 | 735 | 279 | 219 | 225 | 61 | 61 | 2 | 0 | 0 | 0 | 0 | 0 | 15 | 735 | 735 | 355 | 355 | 48 | 48 | 23 | 23 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 539 | 735 | 227 | 229 | 735 | 228 | 229 | 735 | 197 | 197 | 170 | 152 | 735 | 10 | 10 | 9 | 7 | 0 | 6 | 517 | 9 | 0 | 2 | 0 | 0 | 7 | 7 | 6 | 26 | 224 | 163 | 90 | 735 | 735 | 735 | 735 | 735 | 95 |
| Argentina | 815 | 815 | 815 | 815 | 1 | 815 | 39 | 815 | 815 | 815 | 815 | 815 | 800 | 800 | 815 | 815 | 16 | 30 | 815 | 815 | 815 | 815 | 78 | 78 | 815 | 815 | 815 | 815 | 815 | 0 | 0 | 0 | 0 | 815 | 815 | 763 | 763 | 441 | 808 | 812 | 812 | 6 | 6 | 4 | 4 | 5 | 5 | 4 | 4 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 815 | 10 | 0 | 0 | 0 | 0 | 20 | 815 | 0 | 0 | 815 | 147 | 29 | 30 | 19 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 815 | 815 | 671 | 671 | 32 | 32 | 26 | 26 | 4 | 4 | 4 | 4 | 1 | 1 | 1 | 1 | 696 | 735 | 49 | 43 | 737 | 48 | 41 | 815 | 233 | 233 | 152 | 55 | 815 | 84 | 84 | 37 | 34 | 1 | 72 | 786 | 32 | 0 | 13 | 0 | 0 | 59 | 59 | 56 | 6 | 30 | 25 | 13 | 815 | 815 | 815 | 815 | 815 | 33 |
| Kenya | 683 | 683 | 683 | 683 | 24 | 683 | 6 | 683 | 683 | 683 | 683 | 683 | 664 | 664 | 683 | 683 | 228 | 607 | 683 | 683 | 683 | 683 | 77 | 77 | 683 | 683 | 683 | 683 | 683 | 68 | 68 | 9 | 9 | 683 | 683 | 657 | 657 | 639 | 683 | 683 | 683 | 67 | 67 | 66 | 66 | 61 | 67 | 66 | 66 | 9 | 9 | 8 | 8 | 8 | 9 | 9 | 9 | 683 | 8 | 5 | 0 | 0 | 0 | 222 | 683 | 5 | 0 | 683 | 594 | 601 | 607 | 136 | 136 | 5 | 0 | 0 | 0 | 0 | 0 | 7 | 683 | 683 | 628 | 628 | 119 | 119 | 114 | 114 | 21 | 21 | 21 | 21 | 0 | 0 | 0 | 0 | 330 | 666 | 610 | 599 | 637 | 609 | 587 | 683 | 283 | 283 | 244 | 327 | 683 | 70 | 70 | 33 | 37 | 3 | 2 | 147 | 5 | 5 | 5 | 5 | 1 | 70 | 70 | 70 | 175 | 607 | 482 | 358 | 683 | 683 | 683 | 683 | 683 | 124 |
| Democratic Republic of the Congo | 775 | 775 | 775 | 775 | 70 | 775 | 16 | 775 | 775 | 775 | 775 | 775 | 768 | 768 | 775 | 775 | 374 | 774 | 775 | 775 | 775 | 775 | 118 | 118 | 775 | 775 | 775 | 775 | 775 | 136 | 136 | 17 | 17 | 775 | 775 | 764 | 764 | 692 | 775 | 775 | 775 | 114 | 114 | 108 | 108 | 105 | 114 | 112 | 112 | 18 | 18 | 16 | 16 | 16 | 18 | 18 | 18 | 775 | 13 | 35 | 0 | 2 | 0 | 244 | 775 | 35 | 2 | 775 | 731 | 769 | 774 | 34 | 34 | 35 | 0 | 0 | 2 | 0 | 0 | 10 | 775 | 775 | 508 | 508 | 149 | 149 | 127 | 127 | 25 | 25 | 23 | 23 | 0 | 0 | 0 | 0 | 246 | 644 | 772 | 736 | 584 | 772 | 717 | 775 | 323 | 323 | 238 | 360 | 775 | 246 | 244 | 45 | 199 | 1 | 2 | 251 | 17 | 17 | 17 | 17 | 12 | 246 | 246 | 245 | 261 | 774 | 422 | 229 | 775 | 775 | 775 | 775 | 775 | 224 |
| Burundi | 613 | 613 | 613 | 613 | 5 | 613 | 3 | 613 | 613 | 613 | 613 | 613 | 590 | 590 | 613 | 613 | 184 | 391 | 613 | 613 | 613 | 613 | 70 | 70 | 613 | 613 | 613 | 613 | 613 | 24 | 24 | 0 | 0 | 613 | 613 | 587 | 587 | 438 | 613 | 611 | 611 | 59 | 59 | 57 | 57 | 54 | 58 | 57 | 57 | 6 | 6 | 6 | 6 | 5 | 6 | 6 | 6 | 613 | 128 | 11 | 9 | 0 | 0 | 191 | 613 | 11 | 0 | 613 | 288 | 364 | 391 | 18 | 18 | 11 | 0 | 0 | 0 | 0 | 0 | 21 | 613 | 613 | 506 | 506 | 78 | 78 | 73 | 73 | 8 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 289 | 601 | 460 | 382 | 572 | 462 | 364 | 613 | 133 | 133 | 55 | 145 | 613 | 23 | 23 | 11 | 17 | 2 | 2 | 246 | 3 | 3 | 3 | 3 | 2 | 23 | 23 | 22 | 113 | 391 | 239 | 130 | 613 | 613 | 613 | 613 | 613 | 117 |
| Iran | 684 | 684 | 684 | 684 | 2 | 684 | 7 | 684 | 684 | 684 | 684 | 684 | 656 | 656 | 684 | 684 | 43 | 167 | 684 | 684 | 684 | 684 | 81 | 81 | 684 | 684 | 684 | 684 | 684 | 9 | 9 | 1 | 1 | 684 | 684 | 648 | 648 | 363 | 680 | 681 | 681 | 15 | 15 | 14 | 14 | 13 | 15 | 15 | 15 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 684 | 48 | 4 | 0 | 2 | 0 | 126 | 684 | 4 | 2 | 684 | 214 | 154 | 167 | 56 | 56 | 4 | 2 | 2 | 2 | 1 | 1 | 30 | 684 | 684 | 565 | 565 | 40 | 40 | 37 | 37 | 11 | 11 | 8 | 8 | 1 | 1 | 1 | 1 | 526 | 581 | 178 | 172 | 572 | 178 | 166 | 684 | 208 | 208 | 58 | 129 | 684 | 35 | 35 | 7 | 16 | 7 | 13 | 534 | 2 | 0 | 0 | 0 | 1 | 27 | 27 | 21 | 45 | 167 | 137 | 86 | 684 | 684 | 684 | 684 | 684 | 18 |
| Mali | 566 | 566 | 566 | 566 | 15 | 566 | 5 | 566 | 566 | 566 | 566 | 566 | 539 | 539 | 566 | 566 | 144 | 533 | 566 | 566 | 566 | 566 | 184 | 184 | 566 | 566 | 566 | 566 | 566 | 43 | 43 | 0 | 0 | 566 | 566 | 555 | 555 | 551 | 565 | 563 | 563 | 64 | 64 | 63 | 63 | 63 | 64 | 63 | 63 | 11 | 11 | 11 | 11 | 10 | 11 | 11 | 11 | 566 | 31 | 55 | 0 | 6 | 0 | 115 | 566 | 55 | 6 | 566 | 527 | 533 | 533 | 167 | 167 | 55 | 15 | 15 | 6 | 2 | 2 | 17 | 566 | 566 | 471 | 471 | 59 | 59 | 53 | 53 | 9 | 9 | 9 | 9 | 0 | 0 | 0 | 0 | 179 | 533 | 533 | 525 | 498 | 533 | 511 | 566 | 225 | 225 | 214 | 252 | 566 | 76 | 76 | 22 | 55 | 0 | 0 | 107 | 3 | 3 | 3 | 3 | 5 | 74 | 74 | 74 | 151 | 533 | 381 | 278 | 566 | 566 | 566 | 566 | 566 | 75 |
| Myanmar | 546 | 546 | 546 | 546 | 20 | 546 | 3 | 546 | 546 | 546 | 534 | 543 | 506 | 506 | 546 | 546 | 187 | 358 | 546 | 546 | 546 | 546 | 80 | 80 | 546 | 546 | 546 | 546 | 546 | 53 | 53 | 3 | 3 | 546 | 546 | 512 | 512 | 480 | 539 | 545 | 545 | 32 | 32 | 32 | 32 | 30 | 32 | 32 | 32 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 546 | 15 | 30 | 0 | 21 | 0 | 183 | 546 | 30 | 21 | 546 | 352 | 350 | 358 | 68 | 68 | 29 | 12 | 12 | 21 | 12 | 12 | 17 | 546 | 546 | 464 | 464 | 80 | 80 | 78 | 78 | 36 | 36 | 36 | 36 | 0 | 0 | 0 | 0 | 303 | 524 | 358 | 356 | 483 | 358 | 325 | 546 | 196 | 196 | 140 | 168 | 546 | 45 | 45 | 14 | 28 | 2 | 6 | 224 | 3 | 1 | 1 | 1 | 2 | 39 | 39 | 39 | 130 | 358 | 253 | 155 | 546 | 546 | 546 | 546 | 546 | 202 |
| Mexico | 524 | 524 | 524 | 524 | 3 | 524 | 27 | 524 | 524 | 524 | 505 | 524 | 521 | 521 | 524 | 524 | 50 | 123 | 524 | 524 | 524 | 524 | 72 | 72 | 524 | 524 | 524 | 524 | 524 | 2 | 2 | 0 | 0 | 524 | 524 | 487 | 487 | 394 | 523 | 522 | 522 | 21 | 21 | 13 | 13 | 15 | 21 | 18 | 18 | 5 | 5 | 2 | 2 | 1 | 5 | 5 | 5 | 524 | 6 | 0 | 0 | 0 | 0 | 76 | 524 | 0 | 0 | 524 | 196 | 119 | 123 | 33 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 524 | 524 | 403 | 403 | 25 | 25 | 23 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 342 | 505 | 144 | 131 | 497 | 145 | 128 | 524 | 109 | 109 | 82 | 46 | 524 | 108 | 108 | 34 | 38 | 1 | 79 | 420 | 30 | 1 | 17 | 1 | 0 | 49 | 49 | 46 | 58 | 123 | 84 | 43 | 524 | 524 | 524 | 524 | 524 | 47 |
| Angola | 499 | 499 | 499 | 499 | 3 | 499 | 7 | 499 | 499 | 499 | 499 | 499 | 465 | 465 | 499 | 499 | 55 | 133 | 499 | 499 | 499 | 499 | 65 | 65 | 499 | 499 | 499 | 499 | 499 | 32 | 32 | 2 | 2 | 499 | 499 | 494 | 494 | 289 | 495 | 496 | 496 | 41 | 41 | 32 | 32 | 31 | 39 | 41 | 41 | 5 | 5 | 4 | 4 | 2 | 5 | 5 | 5 | 499 | 1 | 0 | 0 | 0 | 0 | 127 | 499 | 0 | 0 | 499 | 31 | 113 | 133 | 12 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 499 | 499 | 419 | 419 | 36 | 36 | 27 | 27 | 5 | 5 | 4 | 4 | 0 | 0 | 0 | 0 | 361 | 482 | 142 | 130 | 462 | 141 | 124 | 499 | 253 | 253 | 172 | 82 | 499 | 49 | 49 | 7 | 29 | 0 | 13 | 392 | 1 | 1 | 1 | 1 | 1 | 30 | 30 | 27 | 33 | 133 | 54 | 24 | 499 | 499 | 499 | 499 | 499 | 170 |
| West Germany (FRG) | 541 | 541 | 541 | 541 | 0 | 541 | 4 | 541 | 541 | 541 | 541 | 541 | 528 | 528 | 541 | 541 | 0 | 0 | 541 | 541 | 541 | 541 | 105 | 105 | 541 | 541 | 541 | 541 | 541 | 0 | 0 | 0 | 0 | 541 | 541 | 510 | 510 | 111 | 535 | 536 | 536 | 6 | 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 541 | 15 | 1 | 0 | 0 | 0 | 0 | 541 | 1 | 0 | 541 | 59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 541 | 541 | 364 | 364 | 24 | 24 | 19 | 19 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 507 | 444 | 9 | 14 | 444 | 9 | 12 | 541 | 239 | 239 | 148 | 57 | 541 | 14 | 14 | 3 | 2 | 3 | 10 | 541 | 6 | 0 | 2 | 0 | 0 | 10 | 10 | 7 | 0 | 0 | 0 | 0 | 541 | 541 | 541 | 541 | 541 | 3 |
| Uganda | 394 | 394 | 394 | 394 | 4 | 394 | 4 | 394 | 394 | 394 | 394 | 394 | 382 | 382 | 394 | 394 | 91 | 221 | 394 | 394 | 394 | 394 | 46 | 46 | 394 | 394 | 394 | 394 | 394 | 46 | 46 | 1 | 1 | 394 | 394 | 369 | 369 | 272 | 393 | 390 | 390 | 35 | 35 | 30 | 30 | 28 | 31 | 31 | 31 | 5 | 5 | 4 | 4 | 3 | 4 | 4 | 4 | 394 | 6 | 4 | 0 | 0 | 0 | 191 | 394 | 4 | 0 | 394 | 125 | 188 | 221 | 11 | 11 | 3 | 0 | 0 | 0 | 0 | 0 | 24 | 394 | 394 | 305 | 305 | 49 | 49 | 41 | 41 | 6 | 6 | 5 | 5 | 0 | 0 | 0 | 0 | 206 | 383 | 223 | 212 | 346 | 221 | 193 | 394 | 130 | 130 | 28 | 100 | 394 | 68 | 67 | 19 | 48 | 3 | 6 | 227 | 1 | 0 | 0 | 0 | 0 | 60 | 60 | 60 | 79 | 221 | 128 | 68 | 394 | 394 | 394 | 394 | 394 | 39 |
| Japan | 402 | 402 | 402 | 402 | 1 | 402 | 3 | 402 | 402 | 402 | 386 | 402 | 395 | 395 | 402 | 402 | 19 | 44 | 402 | 402 | 402 | 402 | 38 | 38 | 402 | 402 | 402 | 402 | 402 | 2 | 2 | 0 | 0 | 402 | 402 | 365 | 365 | 246 | 401 | 399 | 399 | 3 | 3 | 2 | 2 | 3 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 402 | 6 | 0 | 0 | 0 | 0 | 40 | 402 | 0 | 0 | 402 | 90 | 44 | 44 | 23 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 402 | 402 | 191 | 191 | 26 | 26 | 23 | 23 | 6 | 6 | 4 | 4 | 1 | 1 | 1 | 1 | 374 | 373 | 63 | 61 | 375 | 62 | 60 | 402 | 98 | 98 | 62 | 45 | 402 | 7 | 7 | 5 | 4 | 1 | 4 | 369 | 1 | 0 | 0 | 0 | 0 | 4 | 4 | 3 | 14 | 42 | 40 | 31 | 402 | 402 | 402 | 402 | 402 | 36 |
| Saudi Arabia | 371 | 371 | 371 | 371 | 41 | 371 | 0 | 371 | 371 | 371 | 371 | 370 | 360 | 360 | 371 | 371 | 75 | 352 | 371 | 371 | 371 | 371 | 87 | 87 | 371 | 371 | 371 | 371 | 371 | 14 | 14 | 0 | 0 | 371 | 371 | 359 | 359 | 364 | 366 | 370 | 370 | 40 | 40 | 40 | 40 | 39 | 40 | 39 | 39 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 371 | 3 | 1 | 0 | 0 | 0 | 83 | 371 | 1 | 0 | 371 | 333 | 343 | 352 | 110 | 110 | 1 | 0 | 0 | 0 | 0 | 0 | 11 | 371 | 371 | 360 | 360 | 41 | 41 | 35 | 35 | 7 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 220 | 360 | 354 | 349 | 335 | 351 | 346 | 371 | 117 | 117 | 92 | 137 | 371 | 15 | 15 | 5 | 4 | 5 | 7 | 28 | 2 | 0 | 0 | 0 | 0 | 15 | 15 | 13 | 110 | 352 | 292 | 223 | 371 | 371 | 371 | 371 | 371 | 31 |
| Mozambique | 363 | 363 | 363 | 363 | 8 | 363 | 8 | 363 | 363 | 363 | 363 | 363 | 333 | 333 | 363 | 363 | 51 | 144 | 363 | 363 | 363 | 363 | 46 | 46 | 363 | 363 | 363 | 363 | 363 | 9 | 9 | 0 | 0 | 363 | 363 | 354 | 354 | 283 | 363 | 361 | 361 | 22 | 22 | 21 | 21 | 22 | 22 | 22 | 22 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 363 | 7 | 0 | 0 | 0 | 0 | 36 | 363 | 0 | 0 | 363 | 152 | 144 | 144 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 363 | 363 | 315 | 315 | 39 | 39 | 35 | 35 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 225 | 347 | 146 | 145 | 342 | 146 | 143 | 363 | 105 | 105 | 101 | 93 | 363 | 34 | 34 | 11 | 9 | 0 | 14 | 230 | 0 | 0 | 0 | 0 | 0 | 18 | 18 | 17 | 34 | 144 | 91 | 47 | 363 | 363 | 363 | 363 | 363 | 37 |
| Cameroon | 332 | 332 | 332 | 332 | 22 | 332 | 2 | 332 | 332 | 332 | 332 | 332 | 322 | 322 | 332 | 332 | 107 | 314 | 332 | 332 | 332 | 332 | 50 | 50 | 332 | 332 | 332 | 332 | 332 | 45 | 45 | 9 | 9 | 332 | 332 | 310 | 310 | 322 | 332 | 332 | 332 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 332 | 5 | 2 | 0 | 0 | 0 | 33 | 332 | 2 | 0 | 332 | 314 | 313 | 314 | 27 | 27 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 332 | 332 | 253 | 253 | 44 | 44 | 33 | 33 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 71 | 312 | 313 | 304 | 259 | 313 | 276 | 332 | 95 | 95 | 91 | 147 | 332 | 64 | 64 | 11 | 52 | 8 | 8 | 81 | 7 | 6 | 5 | 5 | 10 | 63 | 63 | 63 | 106 | 314 | 231 | 148 | 332 | 332 | 332 | 332 | 332 | 76 |
| Ireland | 307 | 307 | 307 | 307 | 6 | 307 | 8 | 307 | 307 | 307 | 307 | 307 | 305 | 305 | 307 | 307 | 91 | 173 | 307 | 307 | 307 | 307 | 29 | 29 | 307 | 307 | 307 | 307 | 307 | 0 | 0 | 0 | 0 | 307 | 307 | 220 | 220 | 218 | 307 | 305 | 305 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 307 | 1 | 0 | 0 | 0 | 0 | 28 | 307 | 0 | 0 | 307 | 185 | 169 | 173 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 307 | 307 | 252 | 252 | 9 | 9 | 6 | 6 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 202 | 299 | 176 | 175 | 258 | 176 | 173 | 307 | 42 | 42 | 30 | 37 | 307 | 18 | 18 | 8 | 5 | 1 | 15 | 134 | 5 | 0 | 1 | 0 | 0 | 11 | 11 | 8 | 16 | 173 | 137 | 70 | 307 | 307 | 307 | 307 | 307 | 41 |
| Honduras | 323 | 323 | 323 | 323 | 0 | 323 | 6 | 323 | 323 | 323 | 310 | 323 | 319 | 319 | 323 | 323 | 10 | 16 | 323 | 323 | 323 | 323 | 56 | 56 | 323 | 323 | 323 | 323 | 323 | 0 | 0 | 0 | 0 | 323 | 323 | 303 | 303 | 202 | 317 | 322 | 322 | 8 | 8 | 5 | 5 | 6 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 323 | 38 | 1 | 0 | 0 | 0 | 12 | 323 | 1 | 0 | 323 | 64 | 16 | 16 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 323 | 323 | 286 | 286 | 24 | 24 | 21 | 21 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 280 | 293 | 23 | 22 | 291 | 29 | 21 | 323 | 83 | 83 | 38 | 18 | 323 | 29 | 29 | 6 | 3 | 2 | 24 | 308 | 7 | 0 | 1 | 0 | 0 | 10 | 10 | 9 | 2 | 16 | 13 | 7 | 323 | 323 | 323 | 323 | 323 | 11 |
| Bolivia | 314 | 314 | 314 | 314 | 0 | 314 | 5 | 314 | 314 | 314 | 314 | 314 | 304 | 304 | 314 | 314 | 3 | 9 | 314 | 314 | 314 | 314 | 13 | 13 | 314 | 314 | 314 | 314 | 314 | 1 | 1 | 0 | 0 | 314 | 314 | 301 | 301 | 137 | 314 | 313 | 313 | 3 | 3 | 2 | 2 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 314 | 8 | 0 | 0 | 0 | 0 | 9 | 314 | 0 | 0 | 314 | 24 | 9 | 9 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 314 | 314 | 281 | 281 | 8 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 285 | 282 | 9 | 10 | 280 | 9 | 10 | 314 | 79 | 79 | 34 | 28 | 314 | 16 | 16 | 4 | 5 | 0 | 11 | 306 | 1 | 0 | 0 | 0 | 0 | 8 | 8 | 6 | 2 | 9 | 7 | 4 | 314 | 314 | 314 | 314 | 314 | 39 |
| Venezuela | 293 | 293 | 293 | 293 | 2 | 293 | 20 | 293 | 293 | 293 | 293 | 293 | 283 | 283 | 293 | 293 | 17 | 62 | 293 | 293 | 293 | 293 | 48 | 48 | 293 | 293 | 293 | 293 | 293 | 4 | 4 | 0 | 0 | 293 | 293 | 278 | 278 | 213 | 290 | 291 | 291 | 11 | 11 | 10 | 10 | 9 | 11 | 11 | 11 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 293 | 10 | 1 | 0 | 0 | 0 | 40 | 293 | 1 | 0 | 293 | 94 | 60 | 62 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 293 | 293 | 206 | 206 | 16 | 16 | 14 | 14 | 7 | 7 | 7 | 7 | 1 | 1 | 1 | 1 | 191 | 281 | 71 | 68 | 276 | 71 | 66 | 293 | 76 | 76 | 44 | 35 | 293 | 64 | 64 | 17 | 18 | 8 | 56 | 239 | 22 | 0 | 10 | 0 | 0 | 30 | 30 | 30 | 18 | 62 | 43 | 23 | 293 | 293 | 293 | 293 | 293 | 35 |
| Central African Republic | 283 | 283 | 283 | 283 | 20 | 283 | 0 | 283 | 283 | 283 | 283 | 283 | 266 | 266 | 283 | 283 | 94 | 274 | 283 | 283 | 283 | 283 | 27 | 27 | 283 | 283 | 283 | 283 | 283 | 51 | 51 | 4 | 4 | 283 | 283 | 281 | 281 | 260 | 283 | 283 | 283 | 33 | 33 | 33 | 33 | 31 | 33 | 33 | 33 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 283 | 0 | 10 | 0 | 0 | 0 | 83 | 283 | 10 | 0 | 283 | 272 | 273 | 274 | 11 | 11 | 10 | 0 | 0 | 0 | 0 | 0 | 1 | 283 | 283 | 217 | 217 | 56 | 56 | 50 | 50 | 8 | 8 | 6 | 6 | 0 | 0 | 0 | 0 | 67 | 260 | 275 | 257 | 222 | 275 | 250 | 283 | 95 | 95 | 79 | 134 | 283 | 73 | 73 | 20 | 53 | 0 | 0 | 82 | 1 | 1 | 1 | 1 | 5 | 73 | 73 | 73 | 67 | 274 | 190 | 121 | 283 | 283 | 283 | 283 | 283 | 36 |
| Brazil | 273 | 273 | 273 | 273 | 1 | 273 | 20 | 273 | 273 | 273 | 273 | 273 | 266 | 266 | 273 | 273 | 6 | 31 | 273 | 273 | 273 | 273 | 31 | 31 | 273 | 273 | 273 | 273 | 273 | 2 | 2 | 0 | 0 | 273 | 273 | 247 | 247 | 188 | 273 | 270 | 270 | 4 | 4 | 3 | 3 | 3 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 273 | 3 | 0 | 0 | 0 | 0 | 20 | 273 | 0 | 0 | 273 | 85 | 30 | 32 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 273 | 273 | 206 | 206 | 10 | 10 | 7 | 7 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 193 | 257 | 34 | 36 | 253 | 33 | 34 | 273 | 36 | 36 | 14 | 13 | 273 | 50 | 50 | 20 | 20 | 1 | 42 | 247 | 12 | 1 | 2 | 1 | 1 | 21 | 21 | 17 | 7 | 31 | 15 | 9 | 273 | 273 | 273 | 273 | 273 | 11 |
| Cambodia | 259 | 259 | 259 | 259 | 0 | 259 | 11 | 259 | 259 | 259 | 239 | 259 | 206 | 206 | 259 | 259 | 15 | 35 | 259 | 259 | 259 | 259 | 28 | 28 | 259 | 259 | 259 | 259 | 259 | 0 | 0 | 0 | 0 | 259 | 259 | 254 | 254 | 235 | 259 | 258 | 258 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 259 | 1 | 0 | 0 | 0 | 0 | 34 | 259 | 0 | 0 | 259 | 57 | 26 | 35 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 259 | 259 | 219 | 219 | 12 | 12 | 12 | 12 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 199 | 247 | 53 | 37 | 246 | 53 | 36 | 259 | 16 | 16 | 2 | 8 | 259 | 40 | 40 | 16 | 11 | 0 | 30 | 226 | 3 | 0 | 3 | 0 | 0 | 12 | 12 | 10 | 12 | 35 | 21 | 14 | 259 | 259 | 259 | 259 | 259 | 65 |
| China | 252 | 252 | 252 | 252 | 1 | 252 | 4 | 252 | 252 | 252 | 244 | 252 | 251 | 251 | 252 | 252 | 66 | 141 | 252 | 252 | 252 | 252 | 20 | 20 | 252 | 252 | 252 | 252 | 252 | 6 | 6 | 1 | 1 | 252 | 252 | 243 | 243 | 214 | 250 | 251 | 251 | 20 | 20 | 20 | 20 | 19 | 20 | 20 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 252 | 2 | 2 | 0 | 0 | 0 | 98 | 252 | 2 | 0 | 252 | 145 | 140 | 141 | 18 | 18 | 2 | 0 | 0 | 0 | 0 | 0 | 9 | 252 | 252 | 225 | 225 | 24 | 24 | 22 | 22 | 7 | 7 | 2 | 2 | 0 | 0 | 0 | 0 | 183 | 246 | 160 | 141 | 239 | 159 | 134 | 252 | 76 | 76 | 41 | 82 | 252 | 15 | 15 | 10 | 7 | 3 | 14 | 116 | 4 | 0 | 0 | 0 | 1 | 7 | 7 | 7 | 64 | 141 | 110 | 74 | 252 | 252 | 252 | 252 | 252 | 89 |
| Georgia | 217 | 217 | 217 | 217 | 0 | 217 | 11 | 217 | 217 | 217 | 212 | 216 | 216 | 216 | 217 | 217 | 82 | 131 | 217 | 217 | 217 | 217 | 30 | 30 | 217 | 217 | 217 | 217 | 217 | 9 | 9 | 0 | 0 | 217 | 217 | 206 | 206 | 152 | 217 | 217 | 217 | 21 | 21 | 17 | 17 | 17 | 21 | 18 | 18 | 8 | 8 | 8 | 8 | 2 | 8 | 7 | 7 | 217 | 0 | 1 | 0 | 0 | 0 | 121 | 217 | 1 | 0 | 217 | 101 | 118 | 131 | 4 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 7 | 217 | 217 | 203 | 203 | 12 | 12 | 11 | 11 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 159 | 217 | 132 | 131 | 214 | 131 | 129 | 217 | 77 | 77 | 8 | 63 | 217 | 22 | 21 | 4 | 13 | 1 | 8 | 100 | 2 | 2 | 2 | 2 | 1 | 15 | 15 | 14 | 18 | 131 | 108 | 54 | 217 | 217 | 217 | 217 | 217 | 14 |
| Haiti | 213 | 213 | 213 | 213 | 0 | 213 | 1 | 213 | 213 | 213 | 208 | 213 | 212 | 212 | 213 | 213 | 13 | 34 | 213 | 213 | 213 | 213 | 25 | 25 | 213 | 213 | 213 | 213 | 213 | 1 | 1 | 0 | 0 | 213 | 213 | 205 | 205 | 169 | 213 | 210 | 210 | 8 | 8 | 6 | 6 | 7 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 213 | 1 | 0 | 0 | 0 | 0 | 33 | 213 | 0 | 0 | 213 | 45 | 32 | 34 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 213 | 213 | 174 | 174 | 9 | 9 | 7 | 7 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 157 | 213 | 35 | 36 | 210 | 38 | 34 | 213 | 16 | 16 | 3 | 10 | 213 | 11 | 11 | 1 | 1 | 0 | 9 | 180 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 13 | 34 | 24 | 19 | 213 | 213 | 213 | 213 | 213 | 14 |
| Ecuador | 220 | 220 | 220 | 220 | 0 | 220 | 10 | 220 | 220 | 220 | 220 | 220 | 213 | 213 | 220 | 220 | 7 | 35 | 220 | 220 | 220 | 220 | 5 | 5 | 220 | 220 | 220 | 220 | 220 | 1 | 1 | 0 | 0 | 220 | 220 | 214 | 214 | 138 | 220 | 219 | 219 | 5 | 5 | 5 | 5 | 3 | 5 | 5 | 5 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 220 | 5 | 3 | 0 | 0 | 0 | 30 | 220 | 3 | 0 | 220 | 55 | 34 | 35 | 11 | 11 | 2 | 2 | 2 | 0 | 0 | 0 | 9 | 220 | 220 | 185 | 185 | 7 | 7 | 5 | 5 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 172 | 207 | 38 | 40 | 206 | 36 | 41 | 220 | 61 | 61 | 28 | 34 | 220 | 30 | 30 | 11 | 15 | 2 | 19 | 189 | 7 | 1 | 4 | 2 | 0 | 15 | 15 | 14 | 7 | 35 | 21 | 11 | 220 | 220 | 220 | 220 | 220 | 8 |
| Bahrain | 207 | 207 | 207 | 207 | 7 | 207 | 0 | 207 | 207 | 207 | 207 | 207 | 199 | 199 | 207 | 207 | 39 | 168 | 207 | 207 | 207 | 207 | 5 | 5 | 207 | 207 | 207 | 207 | 207 | 6 | 6 | 0 | 0 | 207 | 207 | 184 | 184 | 170 | 207 | 206 | 206 | 9 | 9 | 8 | 8 | 8 | 9 | 8 | 8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 207 | 1 | 4 | 0 | 0 | 0 | 50 | 207 | 4 | 0 | 207 | 171 | 168 | 168 | 26 | 26 | 4 | 3 | 3 | 0 | 0 | 0 | 3 | 207 | 207 | 198 | 198 | 13 | 13 | 11 | 11 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 147 | 206 | 168 | 168 | 204 | 168 | 168 | 207 | 77 | 77 | 72 | 83 | 207 | 1 | 1 | 1 | 0 | 0 | 0 | 40 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 13 | 168 | 122 | 88 | 207 | 207 | 207 | 207 | 207 | 16 |
| South Sudan | 225 | 225 | 225 | 225 | 21 | 225 | 0 | 225 | 225 | 225 | 225 | 225 | 223 | 223 | 225 | 225 | 66 | 225 | 225 | 225 | 225 | 225 | 59 | 59 | 225 | 225 | 225 | 225 | 225 | 22 | 22 | 2 | 2 | 225 | 225 | 217 | 217 | 225 | 225 | 225 | 225 | 23 | 23 | 22 | 22 | 23 | 23 | 23 | 23 | 10 | 10 | 9 | 9 | 10 | 10 | 10 | 10 | 225 | 3 | 9 | 0 | 1 | 0 | 25 | 225 | 9 | 1 | 225 | 225 | 225 | 225 | 37 | 37 | 9 | 0 | 0 | 1 | 0 | 0 | 0 | 225 | 225 | 159 | 159 | 31 | 31 | 27 | 27 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 29 | 198 | 224 | 212 | 173 | 224 | 197 | 225 | 78 | 78 | 78 | 99 | 225 | 51 | 51 | 6 | 45 | 0 | 0 | 51 | 3 | 3 | 3 | 3 | 1 | 51 | 51 | 51 | 78 | 225 | 145 | 85 | 225 | 225 | 225 | 225 | 225 | 48 |
| Yugoslavia | 203 | 203 | 203 | 203 | 0 | 203 | 3 | 203 | 203 | 203 | 198 | 203 | 202 | 202 | 203 | 203 | 36 | 106 | 203 | 203 | 203 | 203 | 45 | 45 | 203 | 203 | 203 | 203 | 203 | 9 | 9 | 0 | 0 | 203 | 203 | 198 | 198 | 155 | 202 | 203 | 203 | 7 | 7 | 7 | 7 | 5 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 203 | 0 | 0 | 0 | 0 | 0 | 106 | 203 | 0 | 0 | 203 | 29 | 75 | 106 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 203 | 203 | 192 | 192 | 17 | 17 | 17 | 17 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 111 | 195 | 105 | 102 | 193 | 105 | 99 | 203 | 46 | 46 | 3 | 28 | 203 | 5 | 5 | 0 | 4 | 1 | 2 | 101 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 4 | 23 | 106 | 60 | 21 | 203 | 203 | 203 | 203 | 203 | 10 |
| Kosovo | 196 | 196 | 196 | 196 | 2 | 196 | 2 | 196 | 196 | 196 | 196 | 196 | 194 | 194 | 196 | 196 | 61 | 196 | 196 | 196 | 196 | 196 | 41 | 41 | 196 | 196 | 196 | 196 | 196 | 7 | 7 | 1 | 1 | 196 | 196 | 188 | 188 | 187 | 195 | 193 | 193 | 16 | 16 | 15 | 15 | 16 | 16 | 16 | 16 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 196 | 0 | 0 | 0 | 0 | 0 | 172 | 196 | 0 | 0 | 196 | 88 | 175 | 196 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 196 | 196 | 190 | 190 | 13 | 13 | 11 | 11 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 70 | 191 | 193 | 192 | 185 | 194 | 190 | 196 | 115 | 115 | 28 | 88 | 196 | 3 | 3 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 3 | 3 | 2 | 49 | 196 | 114 | 56 | 196 | 196 | 196 | 196 | 196 | 14 |
| Tajikistan | 188 | 188 | 188 | 188 | 0 | 188 | 7 | 188 | 188 | 188 | 183 | 188 | 179 | 179 | 188 | 188 | 12 | 44 | 188 | 188 | 188 | 188 | 56 | 56 | 188 | 188 | 188 | 188 | 188 | 3 | 3 | 0 | 0 | 188 | 188 | 177 | 177 | 94 | 188 | 187 | 187 | 5 | 5 | 4 | 4 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 188 | 0 | 0 | 0 | 0 | 0 | 36 | 188 | 0 | 0 | 188 | 31 | 42 | 44 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 188 | 188 | 170 | 170 | 8 | 8 | 8 | 8 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 137 | 188 | 46 | 47 | 186 | 46 | 47 | 188 | 22 | 22 | 6 | 13 | 188 | 22 | 22 | 6 | 12 | 3 | 17 | 150 | 1 | 1 | 1 | 1 | 2 | 12 | 12 | 9 | 8 | 44 | 31 | 20 | 188 | 188 | 188 | 188 | 188 | 12 |
| Ethiopia | 190 | 190 | 190 | 190 | 2 | 190 | 23 | 190 | 190 | 190 | 190 | 190 | 170 | 170 | 190 | 190 | 42 | 104 | 190 | 190 | 190 | 190 | 19 | 19 | 190 | 190 | 190 | 190 | 190 | 20 | 20 | 11 | 11 | 190 | 190 | 176 | 176 | 145 | 190 | 186 | 186 | 31 | 31 | 29 | 29 | 22 | 30 | 29 | 29 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | 4 | 190 | 3 | 2 | 0 | 0 | 0 | 66 | 189 | 2 | 0 | 190 | 93 | 101 | 104 | 20 | 20 | 2 | 0 | 0 | 0 | 0 | 0 | 9 | 190 | 190 | 144 | 144 | 8 | 8 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 180 | 111 | 106 | 165 | 110 | 95 | 190 | 62 | 62 | 36 | 53 | 190 | 53 | 53 | 16 | 38 | 18 | 30 | 107 | 7 | 0 | 0 | 0 | 1 | 44 | 44 | 43 | 34 | 104 | 71 | 44 | 190 | 190 | 190 | 190 | 190 | 21 |
| Rwanda | 159 | 159 | 159 | 159 | 0 | 159 | 2 | 159 | 159 | 159 | 159 | 159 | 151 | 151 | 159 | 159 | 22 | 40 | 159 | 159 | 159 | 159 | 10 | 10 | 159 | 159 | 159 | 159 | 159 | 0 | 0 | 0 | 0 | 159 | 159 | 152 | 152 | 79 | 158 | 157 | 157 | 4 | 4 | 4 | 4 | 2 | 4 | 4 | 4 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 159 | 23 | 0 | 0 | 0 | 0 | 28 | 159 | 0 | 0 | 159 | 44 | 36 | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 159 | 159 | 134 | 134 | 13 | 13 | 13 | 13 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 113 | 158 | 40 | 40 | 157 | 41 | 38 | 159 | 15 | 15 | 6 | 17 | 159 | 5 | 5 | 0 | 3 | 0 | 0 | 123 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 13 | 40 | 33 | 23 | 159 | 159 | 159 | 159 | 159 | 9 |
| Bosnia-Herzegovina | 159 | 159 | 159 | 159 | 2 | 159 | 0 | 159 | 159 | 159 | 156 | 159 | 158 | 158 | 159 | 159 | 9 | 47 | 159 | 159 | 159 | 159 | 17 | 17 | 159 | 159 | 159 | 159 | 159 | 1 | 1 | 0 | 0 | 159 | 159 | 149 | 149 | 95 | 156 | 155 | 155 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 159 | 0 | 0 | 0 | 0 | 0 | 40 | 159 | 0 | 0 | 159 | 30 | 31 | 47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 159 | 159 | 150 | 150 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 120 | 156 | 50 | 48 | 156 | 52 | 48 | 159 | 36 | 36 | 7 | 24 | 159 | 2 | 2 | 1 | 1 | 0 | 2 | 113 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 10 | 47 | 30 | 16 | 159 | 159 | 159 | 159 | 159 | 22 |
| Niger | 154 | 154 | 154 | 154 | 5 | 154 | 6 | 154 | 154 | 154 | 146 | 154 | 152 | 152 | 154 | 154 | 32 | 116 | 154 | 154 | 154 | 154 | 34 | 34 | 154 | 154 | 154 | 154 | 154 | 26 | 26 | 1 | 1 | 154 | 154 | 150 | 150 | 137 | 154 | 154 | 154 | 22 | 22 | 20 | 20 | 17 | 21 | 21 | 21 | 4 | 4 | 2 | 2 | 3 | 4 | 4 | 4 | 154 | 25 | 8 | 0 | 1 | 0 | 37 | 154 | 8 | 1 | 154 | 111 | 115 | 116 | 25 | 25 | 8 | 3 | 3 | 1 | 0 | 0 | 12 | 154 | 154 | 113 | 113 | 27 | 27 | 24 | 24 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 61 | 151 | 116 | 112 | 137 | 115 | 107 | 154 | 56 | 56 | 45 | 61 | 154 | 22 | 21 | 3 | 19 | 4 | 4 | 58 | 1 | 1 | 1 | 1 | 2 | 20 | 20 | 19 | 46 | 116 | 95 | 67 | 154 | 154 | 154 | 154 | 154 | 25 |
| Belgium | 154 | 154 | 154 | 154 | 0 | 154 | 3 | 154 | 154 | 154 | 154 | 154 | 154 | 154 | 154 | 154 | 7 | 33 | 154 | 154 | 154 | 154 | 12 | 12 | 154 | 154 | 154 | 154 | 154 | 0 | 0 | 0 | 0 | 154 | 154 | 145 | 145 | 81 | 154 | 153 | 153 | 10 | 10 | 7 | 7 | 5 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 154 | 4 | 0 | 0 | 0 | 0 | 27 | 154 | 0 | 0 | 154 | 51 | 32 | 33 | 11 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 154 | 154 | 115 | 115 | 14 | 14 | 11 | 11 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 132 | 146 | 43 | 41 | 146 | 41 | 41 | 154 | 38 | 38 | 22 | 18 | 154 | 4 | 4 | 1 | 1 | 1 | 5 | 128 | 2 | 0 | 3 | 0 | 0 | 4 | 4 | 3 | 8 | 33 | 25 | 22 | 154 | 154 | 154 | 154 | 154 | 13 |
| Namibia | 151 | 151 | 151 | 151 | 0 | 151 | 1 | 151 | 151 | 151 | 151 | 151 | 135 | 135 | 151 | 151 | 5 | 28 | 151 | 151 | 151 | 151 | 19 | 19 | 151 | 151 | 151 | 151 | 151 | 3 | 3 | 0 | 0 | 151 | 151 | 147 | 147 | 90 | 151 | 150 | 150 | 6 | 6 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 151 | 0 | 0 | 0 | 0 | 0 | 28 | 151 | 0 | 0 | 151 | 17 | 27 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 151 | 151 | 126 | 126 | 13 | 13 | 13 | 13 | 3 | 3 | 3 | 3 | 1 | 1 | 0 | 0 | 110 | 141 | 29 | 31 | 140 | 29 | 27 | 151 | 53 | 53 | 31 | 15 | 151 | 18 | 18 | 2 | 9 | 3 | 10 | 133 | 1 | 0 | 0 | 0 | 0 | 10 | 10 | 10 | 7 | 28 | 11 | 5 | 151 | 151 | 151 | 151 | 151 | 4 |
| Cyprus | 132 | 132 | 132 | 132 | 1 | 132 | 2 | 132 | 132 | 132 | 132 | 132 | 128 | 128 | 132 | 132 | 8 | 28 | 132 | 132 | 132 | 132 | 16 | 16 | 132 | 132 | 132 | 132 | 132 | 1 | 1 | 1 | 1 | 132 | 132 | 125 | 125 | 77 | 132 | 131 | 131 | 3 | 3 | 1 | 1 | 2 | 3 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 132 | 9 | 0 | 0 | 0 | 0 | 9 | 132 | 0 | 0 | 132 | 35 | 27 | 28 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 132 | 132 | 124 | 124 | 5 | 5 | 4 | 4 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 115 | 131 | 29 | 31 | 130 | 29 | 30 | 132 | 43 | 43 | 32 | 25 | 132 | 6 | 6 | 3 | 3 | 1 | 4 | 105 | 2 | 0 | 0 | 0 | 1 | 4 | 4 | 4 | 3 | 28 | 12 | 5 | 132 | 132 | 132 | 132 | 132 | 8 |
| Sweden | 132 | 132 | 132 | 132 | 1 | 132 | 1 | 132 | 132 | 132 | 132 | 132 | 130 | 130 | 132 | 132 | 23 | 87 | 132 | 132 | 132 | 132 | 3 | 3 | 132 | 132 | 132 | 132 | 132 | 0 | 0 | 0 | 0 | 132 | 132 | 129 | 129 | 102 | 130 | 117 | 117 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 132 | 1 | 1 | 0 | 0 | 0 | 44 | 132 | 1 | 0 | 132 | 87 | 87 | 87 | 13 | 13 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 132 | 132 | 113 | 113 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 68 | 131 | 89 | 89 | 128 | 88 | 87 | 132 | 74 | 74 | 64 | 71 | 132 | 4 | 4 | 2 | 1 | 1 | 1 | 45 | 2 | 0 | 1 | 0 | 0 | 2 | 2 | 0 | 11 | 87 | 57 | 35 | 132 | 132 | 132 | 132 | 132 | 12 |
| Netherlands | 130 | 130 | 130 | 130 | 0 | 130 | 9 | 130 | 130 | 130 | 130 | 130 | 126 | 126 | 130 | 130 | 9 | 28 | 130 | 130 | 130 | 130 | 5 | 5 | 130 | 130 | 130 | 130 | 130 | 1 | 1 | 0 | 0 | 130 | 130 | 122 | 122 | 73 | 130 | 130 | 130 | 9 | 9 | 6 | 6 | 7 | 9 | 9 | 9 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 130 | 4 | 0 | 0 | 0 | 0 | 20 | 130 | 0 | 0 | 130 | 43 | 28 | 28 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 130 | 130 | 93 | 93 | 9 | 9 | 8 | 8 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 109 | 126 | 38 | 34 | 125 | 38 | 35 | 130 | 39 | 39 | 26 | 23 | 130 | 11 | 11 | 3 | 7 | 4 | 4 | 107 | 4 | 0 | 0 | 0 | 3 | 9 | 9 | 6 | 8 | 28 | 20 | 10 | 130 | 130 | 130 | 130 | 130 | 9 |
| Panama | 127 | 127 | 127 | 127 | 1 | 127 | 1 | 127 | 127 | 127 | 122 | 127 | 127 | 127 | 127 | 127 | 2 | 4 | 127 | 127 | 127 | 127 | 8 | 8 | 127 | 127 | 127 | 127 | 127 | 0 | 0 | 0 | 0 | 127 | 127 | 119 | 119 | 72 | 126 | 126 | 126 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 127 | 6 | 0 | 0 | 0 | 0 | 3 | 127 | 0 | 0 | 127 | 18 | 4 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 127 | 127 | 104 | 104 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 111 | 124 | 18 | 7 | 124 | 16 | 6 | 127 | 24 | 24 | 17 | 9 | 127 | 7 | 7 | 1 | 1 | 2 | 6 | 123 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 4 | 1 | 4 | 3 | 1 | 127 | 127 | 127 | 127 | 127 | 12 |
| Portugal | 140 | 140 | 140 | 140 | 0 | 140 | 0 | 140 | 140 | 140 | 140 | 140 | 139 | 139 | 140 | 140 | 2 | 3 | 140 | 140 | 140 | 140 | 12 | 12 | 140 | 140 | 140 | 140 | 140 | 0 | 0 | 0 | 0 | 140 | 140 | 120 | 120 | 25 | 140 | 139 | 139 | 2 | 2 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 140 | 1 | 0 | 0 | 0 | 0 | 2 | 140 | 0 | 0 | 140 | 14 | 3 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 140 | 140 | 128 | 128 | 3 | 3 | 3 | 3 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 131 | 124 | 9 | 9 | 123 | 9 | 8 | 140 | 72 | 72 | 42 | 17 | 140 | 4 | 4 | 0 | 0 | 0 | 3 | 137 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 3 | 2 | 0 | 140 | 140 | 140 | 140 | 140 | 25 |
| Senegal | 118 | 118 | 118 | 118 | 0 | 118 | 1 | 118 | 118 | 118 | 118 | 118 | 83 | 83 | 118 | 118 | 26 | 48 | 118 | 118 | 118 | 118 | 29 | 29 | 118 | 118 | 118 | 118 | 118 | 6 | 6 | 1 | 1 | 118 | 118 | 114 | 114 | 81 | 118 | 115 | 115 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 118 | 3 | 0 | 0 | 0 | 0 | 33 | 117 | 0 | 0 | 118 | 37 | 45 | 48 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 118 | 118 | 101 | 101 | 8 | 8 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 78 | 115 | 48 | 44 | 118 | 48 | 44 | 118 | 29 | 29 | 14 | 24 | 118 | 10 | 10 | 3 | 5 | 2 | 5 | 76 | 2 | 1 | 2 | 1 | 1 | 7 | 7 | 6 | 9 | 48 | 26 | 13 | 118 | 118 | 118 | 118 | 118 | 6 |
| Macedonia | 118 | 118 | 118 | 118 | 0 | 118 | 2 | 118 | 118 | 118 | 117 | 117 | 115 | 115 | 118 | 118 | 44 | 114 | 118 | 118 | 118 | 118 | 13 | 13 | 118 | 118 | 118 | 118 | 118 | 6 | 6 | 0 | 0 | 118 | 118 | 116 | 116 | 109 | 118 | 118 | 118 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 118 | 7 | 1 | 0 | 0 | 0 | 110 | 118 | 1 | 0 | 118 | 33 | 100 | 114 | 9 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 7 | 118 | 118 | 106 | 106 | 10 | 10 | 9 | 9 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 33 | 115 | 112 | 112 | 114 | 112 | 109 | 118 | 63 | 63 | 6 | 42 | 118 | 13 | 13 | 2 | 11 | 0 | 0 | 16 | 5 | 5 | 5 | 5 | 5 | 13 | 13 | 12 | 20 | 114 | 78 | 47 | 118 | 118 | 118 | 118 | 118 | 32 |
| Paraguay | 114 | 114 | 114 | 114 | 3 | 114 | 1 | 114 | 114 | 114 | 114 | 114 | 109 | 109 | 114 | 114 | 18 | 82 | 114 | 114 | 114 | 114 | 9 | 9 | 114 | 114 | 114 | 114 | 114 | 13 | 13 | 0 | 0 | 114 | 114 | 113 | 113 | 109 | 114 | 114 | 114 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 114 | 1 | 1 | 0 | 0 | 0 | 29 | 114 | 1 | 0 | 114 | 87 | 82 | 82 | 31 | 31 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 114 | 114 | 99 | 99 | 16 | 16 | 14 | 14 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 47 | 112 | 82 | 82 | 111 | 82 | 81 | 114 | 45 | 45 | 40 | 49 | 114 | 24 | 24 | 13 | 11 | 0 | 1 | 55 | 9 | 7 | 7 | 7 | 2 | 23 | 23 | 23 | 17 | 82 | 53 | 24 | 114 | 114 | 114 | 114 | 114 | 22 |
| Australia | 114 | 114 | 114 | 114 | 0 | 114 | 1 | 114 | 114 | 114 | 108 | 114 | 109 | 109 | 114 | 114 | 25 | 58 | 114 | 114 | 114 | 114 | 13 | 13 | 114 | 114 | 114 | 114 | 114 | 0 | 0 | 0 | 0 | 114 | 114 | 111 | 111 | 89 | 114 | 114 | 114 | 3 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 114 | 1 | 0 | 0 | 0 | 0 | 28 | 114 | 0 | 0 | 114 | 62 | 57 | 58 | 12 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 114 | 114 | 94 | 94 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 77 | 111 | 66 | 59 | 110 | 65 | 58 | 114 | 46 | 46 | 31 | 38 | 114 | 3 | 3 | 3 | 1 | 0 | 0 | 61 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 12 | 58 | 44 | 24 | 114 | 114 | 114 | 114 | 114 | 15 |
| Austria | 115 | 115 | 115 | 115 | 2 | 115 | 3 | 115 | 115 | 115 | 115 | 115 | 114 | 114 | 115 | 115 | 15 | 24 | 115 | 115 | 115 | 115 | 1 | 1 | 115 | 115 | 115 | 115 | 115 | 1 | 1 | 0 | 0 | 115 | 115 | 106 | 106 | 51 | 115 | 111 | 111 | 3 | 3 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 115 | 4 | 0 | 0 | 0 | 0 | 16 | 115 | 0 | 0 | 115 | 38 | 23 | 24 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 115 | 115 | 91 | 91 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 104 | 110 | 29 | 28 | 110 | 29 | 28 | 115 | 30 | 30 | 14 | 25 | 115 | 5 | 5 | 3 | 3 | 1 | 4 | 91 | 2 | 0 | 1 | 0 | 0 | 2 | 2 | 2 | 2 | 24 | 20 | 10 | 115 | 115 | 115 | 115 | 115 | 5 |
| Jordan | 113 | 113 | 113 | 113 | 3 | 113 | 6 | 113 | 113 | 113 | 113 | 113 | 107 | 107 | 113 | 113 | 20 | 47 | 113 | 113 | 113 | 113 | 14 | 14 | 113 | 113 | 113 | 113 | 113 | 0 | 0 | 0 | 0 | 113 | 113 | 110 | 110 | 76 | 113 | 113 | 113 | 11 | 11 | 11 | 11 | 11 | 11 | 10 | 10 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 113 | 4 | 0 | 0 | 0 | 0 | 23 | 113 | 0 | 0 | 113 | 55 | 45 | 47 | 19 | 19 | 1 | 1 | 1 | 0 | 0 | 0 | 10 | 113 | 113 | 99 | 99 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 109 | 48 | 50 | 110 | 45 | 48 | 113 | 34 | 34 | 22 | 22 | 113 | 8 | 8 | 6 | 5 | 0 | 4 | 67 | 2 | 0 | 0 | 0 | 0 | 7 | 7 | 6 | 17 | 47 | 38 | 26 | 113 | 113 | 113 | 113 | 113 | 18 |
| Tunisia | 109 | 109 | 109 | 109 | 1 | 109 | 2 | 109 | 109 | 109 | 108 | 109 | 104 | 104 | 109 | 109 | 32 | 97 | 109 | 109 | 109 | 109 | 49 | 49 | 109 | 109 | 109 | 109 | 109 | 3 | 3 | 0 | 0 | 109 | 109 | 107 | 107 | 105 | 109 | 109 | 109 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 109 | 6 | 8 | 0 | 0 | 0 | 33 | 109 | 8 | 0 | 109 | 102 | 96 | 97 | 33 | 33 | 8 | 0 | 0 | 0 | 0 | 0 | 4 | 109 | 109 | 100 | 100 | 17 | 17 | 14 | 14 | 5 | 5 | 4 | 4 | 0 | 0 | 0 | 0 | 53 | 109 | 98 | 97 | 106 | 98 | 89 | 109 | 36 | 36 | 33 | 47 | 109 | 8 | 8 | 5 | 2 | 2 | 2 | 19 | 1 | 0 | 0 | 0 | 1 | 8 | 8 | 7 | 31 | 97 | 82 | 67 | 109 | 109 | 109 | 109 | 109 | 18 |
| Switzerland | 111 | 111 | 111 | 111 | 1 | 111 | 4 | 111 | 111 | 111 | 111 | 111 | 111 | 111 | 111 | 111 | 5 | 21 | 111 | 111 | 111 | 111 | 4 | 4 | 111 | 111 | 111 | 111 | 111 | 2 | 2 | 0 | 0 | 111 | 111 | 107 | 107 | 49 | 108 | 111 | 111 | 5 | 5 | 4 | 4 | 2 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 111 | 2 | 0 | 0 | 0 | 0 | 17 | 111 | 0 | 0 | 111 | 29 | 18 | 21 | 13 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 111 | 111 | 85 | 85 | 6 | 6 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 | 100 | 25 | 25 | 100 | 25 | 26 | 111 | 33 | 33 | 20 | 17 | 111 | 7 | 7 | 5 | 5 | 2 | 4 | 92 | 2 | 0 | 1 | 0 | 0 | 6 | 6 | 5 | 6 | 21 | 13 | 6 | 111 | 111 | 111 | 111 | 111 | 11 |
| Zimbabwe | 101 | 101 | 101 | 101 | 2 | 101 | 1 | 101 | 101 | 101 | 101 | 101 | 95 | 95 | 101 | 101 | 14 | 29 | 101 | 101 | 101 | 101 | 11 | 11 | 101 | 101 | 101 | 101 | 101 | 2 | 2 | 0 | 0 | 101 | 101 | 99 | 99 | 69 | 101 | 101 | 101 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 2 | 2 | 1 | 1 | 0 | 2 | 2 | 2 | 101 | 10 | 0 | 0 | 0 | 0 | 27 | 101 | 0 | 0 | 101 | 36 | 27 | 29 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 101 | 101 | 82 | 82 | 14 | 14 | 13 | 13 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 81 | 96 | 31 | 28 | 94 | 30 | 26 | 101 | 39 | 39 | 11 | 25 | 101 | 6 | 6 | 3 | 0 | 0 | 3 | 73 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 6 | 29 | 24 | 13 | 101 | 101 | 101 | 101 | 101 | 2 |
| Sierra Leone | 101 | 101 | 101 | 101 | 6 | 101 | 14 | 101 | 101 | 101 | 101 | 101 | 92 | 92 | 101 | 101 | 16 | 44 | 101 | 101 | 101 | 101 | 15 | 15 | 101 | 101 | 101 | 101 | 101 | 7 | 7 | 2 | 2 | 101 | 101 | 93 | 93 | 83 | 100 | 101 | 101 | 12 | 12 | 8 | 8 | 10 | 12 | 10 | 10 | 3 | 3 | 1 | 1 | 3 | 3 | 2 | 2 | 101 | 0 | 1 | 0 | 0 | 0 | 44 | 101 | 1 | 0 | 101 | 17 | 36 | 44 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 12 | 101 | 101 | 76 | 76 | 11 | 11 | 8 | 8 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 63 | 95 | 41 | 38 | 87 | 42 | 37 | 101 | 17 | 17 | 3 | 14 | 101 | 26 | 26 | 9 | 22 | 0 | 5 | 74 | 0 | 0 | 0 | 0 | 0 | 20 | 20 | 16 | 14 | 44 | 14 | 7 | 101 | 101 | 101 | 101 | 101 | 6 |
| Canada | 96 | 96 | 96 | 96 | 1 | 96 | 2 | 96 | 96 | 96 | 93 | 96 | 94 | 94 | 96 | 96 | 30 | 60 | 96 | 96 | 96 | 96 | 5 | 5 | 96 | 96 | 96 | 96 | 96 | 2 | 2 | 0 | 0 | 96 | 96 | 95 | 95 | 74 | 94 | 96 | 96 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 96 | 1 | 0 | 0 | 0 | 0 | 48 | 96 | 0 | 0 | 96 | 59 | 59 | 62 | 18 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 96 | 96 | 87 | 87 | 9 | 9 | 6 | 6 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 69 | 95 | 65 | 62 | 95 | 64 | 62 | 96 | 40 | 40 | 26 | 33 | 96 | 3 | 3 | 2 | 2 | 0 | 2 | 37 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 10 | 60 | 52 | 36 | 96 | 96 | 96 | 96 | 96 | 8 |
| Malaysia | 99 | 99 | 99 | 99 | 1 | 99 | 4 | 99 | 99 | 99 | 96 | 99 | 99 | 99 | 99 | 99 | 35 | 62 | 99 | 99 | 99 | 99 | 10 | 10 | 99 | 99 | 99 | 99 | 99 | 2 | 2 | 1 | 1 | 99 | 99 | 97 | 97 | 74 | 99 | 99 | 99 | 8 | 8 | 6 | 6 | 7 | 7 | 7 | 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 99 | 2 | 0 | 0 | 0 | 0 | 32 | 99 | 0 | 0 | 99 | 66 | 62 | 62 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 99 | 99 | 90 | 90 | 18 | 18 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73 | 92 | 65 | 65 | 91 | 66 | 65 | 99 | 33 | 33 | 27 | 39 | 99 | 25 | 25 | 2 | 23 | 8 | 5 | 60 | 12 | 12 | 12 | 12 | 5 | 25 | 25 | 22 | 21 | 62 | 48 | 43 | 99 | 99 | 99 | 99 | 99 | 22 |
| Dominican Republic | 90 | 90 | 90 | 90 | 0 | 90 | 3 | 90 | 90 | 90 | 79 | 90 | 90 | 90 | 90 | 90 | 1 | 3 | 90 | 90 | 90 | 90 | 4 | 4 | 90 | 90 | 90 | 90 | 90 | 0 | 0 | 0 | 0 | 90 | 90 | 88 | 88 | 70 | 90 | 89 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 0 | 0 | 0 | 0 | 1 | 90 | 0 | 0 | 90 | 15 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 90 | 75 | 75 | 3 | 3 | 3 | 3 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 76 | 89 | 3 | 4 | 88 | 3 | 3 | 90 | 11 | 11 | 8 | 2 | 90 | 6 | 6 | 4 | 4 | 0 | 5 | 87 | 5 | 0 | 2 | 0 | 0 | 3 | 3 | 3 | 1 | 3 | 2 | 1 | 90 | 90 | 90 | 90 | 90 | 21 |
| Papua New Guinea | 89 | 89 | 89 | 89 | 1 | 89 | 3 | 89 | 89 | 89 | 82 | 89 | 89 | 89 | 89 | 89 | 1 | 10 | 89 | 89 | 89 | 89 | 13 | 13 | 89 | 89 | 89 | 89 | 89 | 1 | 1 | 0 | 0 | 89 | 89 | 86 | 86 | 79 | 89 | 89 | 89 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 89 | 1 | 1 | 0 | 0 | 0 | 3 | 89 | 1 | 0 | 89 | 16 | 10 | 10 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 89 | 64 | 64 | 4 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 62 | 86 | 11 | 10 | 85 | 11 | 10 | 89 | 14 | 14 | 13 | 6 | 89 | 9 | 9 | 5 | 2 | 0 | 7 | 81 | 1 | 0 | 0 | 0 | 0 | 6 | 6 | 5 | 3 | 10 | 5 | 0 | 89 | 89 | 89 | 89 | 89 | 7 |
| Chad | 91 | 91 | 91 | 91 | 1 | 91 | 4 | 91 | 91 | 91 | 91 | 91 | 77 | 77 | 91 | 91 | 34 | 75 | 91 | 91 | 91 | 91 | 21 | 21 | 91 | 91 | 91 | 91 | 91 | 7 | 7 | 0 | 0 | 91 | 91 | 91 | 91 | 83 | 91 | 90 | 90 | 12 | 12 | 10 | 10 | 12 | 12 | 12 | 12 | 3 | 3 | 3 | 3 | 3 | 2 | 3 | 3 | 91 | 0 | 0 | 0 | 0 | 0 | 38 | 91 | 0 | 0 | 91 | 53 | 68 | 75 | 13 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 91 | 91 | 69 | 69 | 7 | 7 | 6 | 6 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 29 | 85 | 75 | 68 | 75 | 75 | 64 | 91 | 17 | 17 | 6 | 30 | 91 | 14 | 14 | 2 | 9 | 2 | 5 | 24 | 1 | 1 | 1 | 1 | 0 | 12 | 12 | 10 | 29 | 75 | 58 | 39 | 91 | 91 | 91 | 91 | 91 | 21 |
| Uruguay | 82 | 82 | 82 | 82 | 0 | 82 | 6 | 82 | 82 | 82 | 82 | 82 | 76 | 76 | 82 | 82 | 0 | 8 | 82 | 82 | 82 | 82 | 3 | 3 | 82 | 82 | 82 | 82 | 82 | 1 | 1 | 0 | 0 | 82 | 82 | 78 | 78 | 42 | 82 | 81 | 81 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 0 | 0 | 0 | 0 | 0 | 2 | 82 | 0 | 0 | 82 | 43 | 8 | 8 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 82 | 54 | 54 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 65 | 78 | 8 | 10 | 78 | 8 | 9 | 82 | 32 | 32 | 31 | 1 | 82 | 14 | 14 | 9 | 8 | 0 | 11 | 74 | 7 | 0 | 1 | 0 | 0 | 7 | 7 | 8 | 1 | 8 | 1 | 1 | 82 | 82 | 82 | 82 | 82 | 6 |
| Soviet Union | 78 | 78 | 78 | 78 | 0 | 78 | 3 | 78 | 78 | 78 | 70 | 78 | 74 | 74 | 78 | 78 | 0 | 0 | 78 | 78 | 78 | 78 | 12 | 12 | 78 | 78 | 78 | 78 | 78 | 0 | 0 | 0 | 0 | 78 | 78 | 77 | 77 | 61 | 78 | 78 | 78 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 78 | 2 | 0 | 0 | 0 | 0 | 0 | 78 | 0 | 0 | 78 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 78 | 78 | 74 | 74 | 4 | 4 | 3 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 74 | 77 | 3 | 2 | 77 | 3 | 1 | 78 | 4 | 4 | 4 | 0 | 78 | 6 | 6 | 3 | 3 | 1 | 7 | 78 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 78 | 78 | 78 | 78 | 78 | 2 |
| Albania | 80 | 80 | 80 | 80 | 0 | 80 | 0 | 80 | 80 | 80 | 71 | 80 | 79 | 79 | 80 | 80 | 5 | 25 | 80 | 80 | 80 | 80 | 6 | 6 | 80 | 80 | 80 | 80 | 80 | 0 | 0 | 0 | 0 | 80 | 80 | 75 | 75 | 62 | 78 | 76 | 76 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 80 | 0 | 0 | 0 | 0 | 0 | 17 | 80 | 0 | 0 | 80 | 22 | 21 | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 80 | 80 | 74 | 74 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 76 | 22 | 22 | 76 | 22 | 21 | 80 | 18 | 18 | 6 | 11 | 80 | 1 | 1 | 0 | 0 | 0 | 1 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 25 | 14 | 10 | 80 | 80 | 80 | 80 | 80 | 6 |
| Kuwait | 76 | 76 | 76 | 76 | 1 | 76 | 3 | 76 | 76 | 76 | 76 | 76 | 61 | 61 | 76 | 76 | 6 | 12 | 76 | 76 | 76 | 76 | 8 | 8 | 76 | 76 | 76 | 76 | 76 | 0 | 0 | 0 | 0 | 76 | 76 | 69 | 69 | 45 | 74 | 76 | 76 | 4 | 4 | 0 | 0 | 3 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 10 | 0 | 0 | 0 | 0 | 11 | 76 | 0 | 0 | 76 | 20 | 12 | 12 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 76 | 76 | 62 | 62 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 61 | 74 | 16 | 16 | 73 | 15 | 16 | 76 | 14 | 14 | 11 | 6 | 76 | 5 | 5 | 3 | 3 | 1 | 4 | 64 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 6 | 12 | 9 | 7 | 76 | 76 | 76 | 76 | 76 | 0 |
| Ivory Coast | 74 | 74 | 74 | 74 | 1 | 74 | 2 | 74 | 74 | 74 | 74 | 74 | 70 | 70 | 74 | 74 | 28 | 59 | 74 | 74 | 74 | 74 | 22 | 22 | 74 | 74 | 74 | 74 | 74 | 10 | 10 | 1 | 1 | 74 | 74 | 72 | 72 | 66 | 74 | 74 | 74 | 11 | 11 | 9 | 9 | 11 | 11 | 10 | 10 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 74 | 1 | 0 | 0 | 0 | 0 | 26 | 74 | 0 | 0 | 74 | 51 | 56 | 59 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 74 | 74 | 60 | 60 | 10 | 10 | 10 | 10 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 26 | 70 | 59 | 58 | 67 | 59 | 57 | 74 | 24 | 24 | 19 | 35 | 74 | 7 | 7 | 4 | 5 | 0 | 4 | 18 | 1 | 0 | 0 | 0 | 0 | 4 | 4 | 3 | 15 | 59 | 52 | 36 | 74 | 74 | 74 | 74 | 74 | 19 |
| Rhodesia | 83 | 83 | 83 | 83 | 0 | 83 | 0 | 83 | 83 | 83 | 83 | 83 | 58 | 58 | 83 | 83 | 0 | 0 | 83 | 83 | 83 | 83 | 3 | 3 | 83 | 83 | 83 | 83 | 83 | 0 | 0 | 0 | 0 | 83 | 83 | 80 | 80 | 21 | 83 | 82 | 82 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 83 | 7 | 0 | 0 | 0 | 0 | 0 | 83 | 0 | 0 | 83 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 83 | 83 | 66 | 66 | 13 | 13 | 11 | 11 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 71 | 69 | 1 | 3 | 67 | 1 | 2 | 83 | 48 | 48 | 26 | 11 | 83 | 9 | 9 | 0 | 0 | 0 | 3 | 83 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 83 | 83 | 83 | 83 | 83 | 0 |
| Suriname | 66 | 66 | 66 | 66 | 0 | 66 | 1 | 66 | 66 | 66 | 66 | 66 | 62 | 62 | 66 | 66 | 0 | 0 | 66 | 66 | 66 | 66 | 16 | 16 | 66 | 66 | 66 | 66 | 66 | 0 | 0 | 0 | 0 | 66 | 66 | 64 | 64 | 51 | 65 | 65 | 65 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 66 | 0 | 0 | 66 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 66 | 66 | 48 | 48 | 3 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 54 | 66 | 0 | 0 | 66 | 0 | 0 | 66 | 5 | 5 | 3 | 5 | 66 | 11 | 11 | 1 | 1 | 1 | 7 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 66 | 66 | 66 | 66 | 66 | 5 |
| Zambia | 62 | 62 | 62 | 62 | 0 | 62 | 0 | 62 | 62 | 62 | 62 | 62 | 54 | 54 | 62 | 62 | 1 | 18 | 62 | 62 | 62 | 62 | 0 | 0 | 62 | 62 | 62 | 62 | 62 | 0 | 0 | 0 | 0 | 62 | 62 | 61 | 61 | 40 | 62 | 62 | 62 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 62 | 0 | 0 | 0 | 0 | 0 | 17 | 62 | 0 | 0 | 62 | 6 | 18 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 62 | 62 | 59 | 59 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 51 | 60 | 18 | 18 | 60 | 18 | 18 | 62 | 20 | 20 | 14 | 11 | 62 | 4 | 4 | 1 | 1 | 0 | 1 | 45 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 15 | 18 | 8 | 6 | 62 | 62 | 62 | 62 | 62 | 13 |
| Croatia | 57 | 57 | 57 | 57 | 0 | 57 | 0 | 57 | 57 | 57 | 48 | 57 | 56 | 56 | 57 | 57 | 7 | 11 | 57 | 57 | 57 | 57 | 6 | 6 | 57 | 57 | 57 | 57 | 57 | 0 | 0 | 0 | 0 | 57 | 57 | 56 | 56 | 39 | 57 | 57 | 57 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 57 | 0 | 0 | 0 | 0 | 0 | 8 | 57 | 0 | 0 | 57 | 9 | 10 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 57 | 56 | 56 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 50 | 57 | 11 | 11 | 57 | 11 | 11 | 57 | 8 | 8 | 1 | 8 | 57 | 1 | 1 | 0 | 0 | 0 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 5 | 11 | 9 | 6 | 57 | 57 | 57 | 57 | 57 | 2 |
| Tanzania | 59 | 59 | 59 | 59 | 4 | 59 | 1 | 59 | 59 | 59 | 59 | 59 | 53 | 53 | 59 | 59 | 22 | 53 | 59 | 59 | 59 | 59 | 3 | 3 | 59 | 59 | 59 | 59 | 59 | 4 | 4 | 0 | 0 | 59 | 59 | 54 | 54 | 56 | 59 | 59 | 59 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 1 | 1 | 0 | 0 | 0 | 21 | 59 | 1 | 0 | 59 | 52 | 53 | 53 | 4 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 59 | 59 | 54 | 54 | 5 | 5 | 5 | 5 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 27 | 56 | 53 | 53 | 55 | 52 | 53 | 59 | 26 | 26 | 21 | 31 | 59 | 7 | 7 | 1 | 5 | 2 | 4 | 11 | 1 | 0 | 0 | 0 | 0 | 7 | 7 | 8 | 13 | 53 | 42 | 29 | 59 | 59 | 59 | 59 | 59 | 6 |
| Costa Rica | 67 | 67 | 67 | 67 | 0 | 67 | 0 | 67 | 67 | 67 | 62 | 67 | 67 | 67 | 67 | 67 | 1 | 1 | 67 | 67 | 67 | 67 | 4 | 4 | 67 | 67 | 67 | 67 | 67 | 0 | 0 | 0 | 0 | 67 | 67 | 64 | 64 | 18 | 67 | 66 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 2 | 0 | 0 | 0 | 0 | 1 | 67 | 0 | 0 | 67 | 11 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 67 | 57 | 57 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 54 | 4 | 3 | 54 | 3 | 3 | 67 | 36 | 36 | 13 | 1 | 67 | 7 | 7 | 0 | 0 | 1 | 7 | 66 | 2 | 0 | 0 | 0 | 0 | 3 | 3 | 2 | 1 | 1 | 1 | 1 | 67 | 67 | 67 | 67 | 67 | 0 |
| Zaire | 50 | 50 | 50 | 50 | 0 | 50 | 0 | 50 | 50 | 50 | 50 | 50 | 44 | 44 | 50 | 50 | 0 | 3 | 50 | 50 | 50 | 50 | 3 | 3 | 50 | 50 | 50 | 50 | 50 | 0 | 0 | 0 | 0 | 50 | 50 | 46 | 46 | 22 | 50 | 50 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 5 | 2 | 0 | 0 | 0 | 0 | 50 | 2 | 0 | 50 | 6 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 50 | 31 | 31 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 34 | 50 | 4 | 5 | 48 | 4 | 5 | 50 | 0 | 0 | 0 | 1 | 50 | 3 | 3 | 0 | 0 | 0 | 3 | 47 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 3 | 3 | 3 | 50 | 50 | 50 | 50 | 50 | 3 |
| Taiwan | 50 | 50 | 50 | 50 | 0 | 50 | 1 | 50 | 50 | 50 | 47 | 50 | 50 | 50 | 50 | 50 | 4 | 10 | 50 | 50 | 50 | 50 | 5 | 5 | 50 | 50 | 50 | 50 | 50 | 1 | 1 | 0 | 0 | 50 | 50 | 49 | 49 | 45 | 50 | 50 | 50 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 7 | 50 | 0 | 0 | 50 | 31 | 10 | 10 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 50 | 26 | 26 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 43 | 50 | 10 | 11 | 49 | 10 | 11 | 50 | 18 | 18 | 15 | 14 | 50 | 4 | 4 | 2 | 1 | 0 | 4 | 40 | 2 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 3 | 10 | 9 | 8 | 50 | 50 | 50 | 50 | 50 | 2 |
| Bulgaria | 52 | 52 | 52 | 52 | 0 | 52 | 1 | 52 | 52 | 52 | 49 | 52 | 52 | 52 | 52 | 52 | 10 | 20 | 52 | 52 | 52 | 52 | 3 | 3 | 52 | 52 | 52 | 52 | 52 | 0 | 0 | 0 | 0 | 52 | 52 | 48 | 48 | 39 | 52 | 52 | 52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 0 | 0 | 0 | 0 | 0 | 13 | 52 | 0 | 0 | 52 | 19 | 20 | 20 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 52 | 52 | 45 | 45 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 50 | 20 | 20 | 51 | 20 | 20 | 52 | 17 | 17 | 8 | 13 | 52 | 1 | 1 | 1 | 1 | 1 | 1 | 32 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 5 | 20 | 15 | 8 | 52 | 52 | 52 | 52 | 52 | 2 |
| Burkina Faso | 52 | 52 | 52 | 52 | 3 | 52 | 0 | 52 | 52 | 52 | 52 | 52 | 51 | 51 | 52 | 52 | 22 | 49 | 52 | 52 | 52 | 52 | 7 | 7 | 52 | 52 | 52 | 52 | 52 | 9 | 9 | 1 | 1 | 52 | 52 | 52 | 52 | 52 | 52 | 52 | 52 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 52 | 4 | 3 | 0 | 2 | 0 | 9 | 52 | 3 | 2 | 52 | 50 | 49 | 49 | 16 | 16 | 3 | 1 | 1 | 2 | 0 | 0 | 1 | 52 | 52 | 45 | 45 | 11 | 11 | 9 | 9 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 16 | 49 | 49 | 49 | 49 | 49 | 49 | 52 | 20 | 20 | 20 | 21 | 52 | 10 | 10 | 5 | 5 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 10 | 15 | 49 | 41 | 28 | 52 | 52 | 52 | 52 | 52 | 13 |
| Togo | 48 | 48 | 48 | 48 | 0 | 48 | 0 | 48 | 48 | 48 | 48 | 48 | 46 | 46 | 48 | 48 | 0 | 1 | 48 | 48 | 48 | 48 | 4 | 4 | 48 | 48 | 48 | 48 | 48 | 0 | 0 | 0 | 0 | 48 | 48 | 46 | 46 | 33 | 47 | 48 | 48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 3 | 0 | 0 | 0 | 0 | 1 | 48 | 0 | 0 | 48 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 48 | 43 | 43 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 40 | 48 | 1 | 1 | 48 | 1 | 1 | 48 | 3 | 3 | 3 | 1 | 48 | 2 | 2 | 0 | 0 | 0 | 2 | 47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 48 | 48 | 48 | 48 | 48 | 2 |
| Azerbaijan | 49 | 49 | 49 | 49 | 1 | 49 | 1 | 49 | 49 | 49 | 45 | 49 | 46 | 46 | 49 | 49 | 9 | 19 | 49 | 49 | 49 | 49 | 4 | 4 | 49 | 49 | 49 | 49 | 49 | 3 | 3 | 1 | 1 | 49 | 49 | 49 | 49 | 38 | 49 | 49 | 49 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 1 | 0 | 0 | 0 | 0 | 12 | 49 | 0 | 0 | 49 | 19 | 19 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 49 | 44 | 44 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 46 | 20 | 19 | 48 | 20 | 19 | 49 | 12 | 12 | 2 | 9 | 49 | 1 | 1 | 1 | 1 | 0 | 2 | 30 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 5 | 19 | 13 | 5 | 49 | 49 | 49 | 49 | 49 | 0 |
| Hungary | 46 | 46 | 46 | 46 | 0 | 46 | 0 | 46 | 46 | 46 | 45 | 46 | 46 | 46 | 46 | 46 | 5 | 9 | 46 | 46 | 46 | 46 | 4 | 4 | 46 | 46 | 46 | 46 | 46 | 2 | 2 | 0 | 0 | 46 | 46 | 38 | 38 | 20 | 46 | 46 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 6 | 46 | 0 | 0 | 46 | 12 | 8 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 46 | 42 | 42 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 44 | 46 | 9 | 9 | 46 | 9 | 9 | 46 | 8 | 8 | 2 | 6 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 9 | 8 | 4 | 46 | 46 | 46 | 46 | 46 | 8 |
| Guadeloupe | 56 | 56 | 56 | 56 | 0 | 56 | 0 | 56 | 56 | 56 | 54 | 56 | 39 | 39 | 56 | 56 | 0 | 0 | 56 | 56 | 56 | 56 | 0 | 0 | 56 | 56 | 56 | 56 | 56 | 0 | 0 | 0 | 0 | 56 | 56 | 51 | 51 | 8 | 56 | 55 | 55 | 3 | 3 | 3 | 3 | 0 | 3 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 1 | 0 | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 56 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 56 | 49 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 46 | 1 | 1 | 45 | 0 | 1 | 56 | 9 | 9 | 6 | 4 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 56 | 56 | 56 | 56 | 3 |
| Denmark | 41 | 41 | 41 | 41 | 0 | 41 | 0 | 41 | 41 | 41 | 41 | 41 | 40 | 40 | 41 | 41 | 4 | 9 | 41 | 41 | 41 | 41 | 2 | 2 | 41 | 41 | 41 | 41 | 41 | 0 | 0 | 0 | 0 | 41 | 41 | 40 | 40 | 26 | 41 | 41 | 41 | 3 | 3 | 3 | 3 | 2 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 2 | 1 | 0 | 0 | 0 | 7 | 41 | 1 | 0 | 41 | 15 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 41 | 25 | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 39 | 9 | 9 | 39 | 10 | 9 | 41 | 5 | 5 | 3 | 6 | 41 | 1 | 1 | 0 | 0 | 0 | 1 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 9 | 8 | 6 | 41 | 41 | 41 | 41 | 41 | 4 |
| Poland | 39 | 39 | 39 | 39 | 0 | 39 | 0 | 39 | 39 | 39 | 36 | 39 | 39 | 39 | 39 | 39 | 1 | 8 | 39 | 39 | 39 | 39 | 0 | 0 | 39 | 39 | 39 | 39 | 39 | 1 | 1 | 0 | 0 | 39 | 39 | 35 | 35 | 25 | 39 | 38 | 38 | 3 | 3 | 2 | 2 | 3 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 6 | 39 | 0 | 0 | 39 | 9 | 8 | 8 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 39 | 32 | 32 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 33 | 38 | 9 | 9 | 38 | 9 | 9 | 39 | 6 | 6 | 2 | 3 | 39 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 4 | 3 | 39 | 39 | 39 | 39 | 39 | 0 |
| Jamaica | 36 | 36 | 36 | 36 | 0 | 36 | 1 | 36 | 36 | 36 | 34 | 36 | 34 | 34 | 36 | 36 | 0 | 4 | 36 | 36 | 36 | 36 | 6 | 6 | 36 | 36 | 36 | 36 | 36 | 0 | 0 | 0 | 0 | 36 | 36 | 34 | 34 | 22 | 36 | 36 | 36 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 0 | 0 | 0 | 0 | 0 | 1 | 36 | 0 | 0 | 36 | 6 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 36 | 30 | 30 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 29 | 36 | 6 | 4 | 33 | 5 | 5 | 36 | 8 | 8 | 3 | 2 | 36 | 1 | 1 | 0 | 0 | 0 | 1 | 32 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 4 | 2 | 1 | 36 | 36 | 36 | 36 | 36 | 0 |
| South Korea | 38 | 38 | 38 | 38 | 0 | 38 | 0 | 38 | 38 | 38 | 32 | 38 | 38 | 38 | 38 | 38 | 2 | 5 | 38 | 38 | 38 | 38 | 6 | 6 | 38 | 38 | 38 | 38 | 38 | 0 | 0 | 0 | 0 | 38 | 38 | 38 | 38 | 33 | 37 | 37 | 37 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 1 | 0 | 0 | 0 | 0 | 5 | 38 | 0 | 0 | 38 | 17 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 38 | 18 | 18 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 30 | 35 | 6 | 5 | 35 | 5 | 4 | 38 | 2 | 2 | 2 | 3 | 38 | 2 | 2 | 0 | 0 | 0 | 1 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 5 | 1 | 38 | 38 | 38 | 38 | 38 | 0 |
| Morocco | 36 | 36 | 36 | 36 | 0 | 36 | 0 | 36 | 36 | 36 | 35 | 36 | 32 | 32 | 36 | 36 | 3 | 13 | 36 | 36 | 36 | 36 | 5 | 5 | 36 | 36 | 36 | 36 | 36 | 1 | 1 | 0 | 0 | 36 | 36 | 29 | 29 | 23 | 36 | 36 | 36 | 4 | 4 | 2 | 2 | 1 | 4 | 4 | 4 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 36 | 6 | 0 | 0 | 0 | 0 | 12 | 36 | 0 | 0 | 36 | 14 | 12 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 36 | 26 | 26 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 35 | 13 | 13 | 32 | 8 | 14 | 36 | 11 | 11 | 3 | 11 | 36 | 3 | 3 | 2 | 0 | 0 | 1 | 24 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 7 | 13 | 9 | 9 | 36 | 36 | 36 | 36 | 36 | 14 |
| Republic of the Congo | 36 | 36 | 36 | 36 | 1 | 36 | 0 | 36 | 36 | 36 | 36 | 36 | 36 | 36 | 36 | 36 | 4 | 24 | 36 | 36 | 36 | 36 | 3 | 3 | 36 | 36 | 36 | 36 | 36 | 11 | 11 | 11 | 11 | 36 | 36 | 35 | 35 | 32 | 36 | 36 | 36 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 36 | 0 | 0 | 0 | 0 | 0 | 20 | 36 | 0 | 0 | 36 | 18 | 24 | 24 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 36 | 36 | 32 | 32 | 14 | 14 | 13 | 13 | 11 | 11 | 11 | 11 | 0 | 0 | 0 | 0 | 11 | 35 | 24 | 23 | 21 | 24 | 23 | 36 | 15 | 15 | 12 | 13 | 36 | 5 | 5 | 0 | 3 | 0 | 2 | 15 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 4 | 14 | 24 | 17 | 14 | 36 | 36 | 36 | 36 | 36 | 11 |
| Kyrgyzstan | 35 | 35 | 35 | 35 | 2 | 35 | 3 | 35 | 35 | 35 | 34 | 35 | 34 | 34 | 35 | 35 | 10 | 30 | 35 | 35 | 35 | 35 | 7 | 7 | 35 | 35 | 35 | 35 | 35 | 1 | 1 | 0 | 0 | 35 | 35 | 32 | 32 | 27 | 35 | 35 | 35 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 0 | 0 | 0 | 0 | 0 | 24 | 35 | 0 | 0 | 35 | 21 | 28 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 35 | 35 | 32 | 32 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 34 | 30 | 29 | 34 | 30 | 29 | 35 | 12 | 12 | 6 | 9 | 35 | 4 | 4 | 0 | 4 | 0 | 0 | 9 | 1 | 1 | 1 | 1 | 0 | 4 | 4 | 4 | 12 | 30 | 18 | 11 | 35 | 35 | 35 | 35 | 35 | 4 |
| Czech Republic | 32 | 32 | 32 | 32 | 1 | 32 | 0 | 32 | 32 | 32 | 31 | 32 | 32 | 32 | 32 | 32 | 8 | 21 | 32 | 32 | 32 | 32 | 2 | 2 | 32 | 32 | 32 | 32 | 32 | 0 | 0 | 0 | 0 | 32 | 32 | 31 | 31 | 21 | 32 | 31 | 31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 0 | 0 | 0 | 0 | 0 | 14 | 32 | 0 | 0 | 32 | 22 | 21 | 21 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 32 | 26 | 26 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 32 | 21 | 21 | 32 | 21 | 21 | 32 | 13 | 13 | 12 | 13 | 32 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 21 | 15 | 6 | 32 | 32 | 32 | 32 | 32 | 2 |
| Macau | 33 | 33 | 33 | 33 | 0 | 33 | 0 | 33 | 33 | 33 | 32 | 33 | 33 | 33 | 33 | 33 | 1 | 6 | 33 | 33 | 33 | 33 | 1 | 1 | 33 | 33 | 33 | 33 | 33 | 0 | 0 | 0 | 0 | 33 | 33 | 31 | 31 | 21 | 33 | 30 | 30 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 6 | 33 | 0 | 0 | 33 | 11 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 33 | 28 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 32 | 6 | 6 | 32 | 6 | 6 | 33 | 6 | 6 | 0 | 5 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 6 | 6 | 5 | 33 | 33 | 33 | 33 | 33 | 3 |
| Liberia | 34 | 34 | 34 | 34 | 1 | 34 | 6 | 34 | 34 | 34 | 34 | 34 | 33 | 33 | 34 | 34 | 5 | 15 | 34 | 34 | 34 | 34 | 4 | 4 | 34 | 34 | 34 | 34 | 34 | 2 | 2 | 0 | 0 | 34 | 34 | 33 | 33 | 26 | 34 | 34 | 34 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 3 | 3 | 2 | 2 | 3 | 3 | 2 | 2 | 34 | 0 | 0 | 0 | 0 | 0 | 14 | 34 | 0 | 0 | 34 | 10 | 13 | 15 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 34 | 34 | 29 | 29 | 3 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 32 | 17 | 14 | 28 | 15 | 14 | 34 | 7 | 7 | 1 | 6 | 34 | 13 | 13 | 6 | 10 | 0 | 4 | 24 | 0 | 0 | 0 | 0 | 0 | 11 | 11 | 10 | 6 | 15 | 12 | 7 | 34 | 34 | 34 | 34 | 34 | 0 |
| New Caledonia | 31 | 31 | 31 | 31 | 0 | 31 | 1 | 31 | 31 | 31 | 28 | 31 | 30 | 30 | 31 | 31 | 0 | 0 | 31 | 31 | 31 | 31 | 1 | 1 | 31 | 31 | 31 | 31 | 31 | 0 | 0 | 0 | 0 | 31 | 31 | 28 | 28 | 11 | 31 | 30 | 30 | 5 | 5 | 5 | 5 | 0 | 5 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 0 | 0 | 31 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 31 | 22 | 22 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 31 | 6 | 6 | 31 | 6 | 6 | 31 | 2 | 2 | 2 | 4 | 31 | 3 | 3 | 2 | 1 | 0 | 2 | 31 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 31 | 31 | 31 | 31 | 31 | 0 |
| Cuba | 30 | 30 | 30 | 30 | 0 | 30 | 0 | 30 | 30 | 30 | 28 | 30 | 29 | 29 | 30 | 30 | 0 | 1 | 30 | 30 | 30 | 30 | 1 | 1 | 30 | 30 | 30 | 30 | 30 | 0 | 0 | 0 | 0 | 30 | 30 | 30 | 30 | 27 | 30 | 30 | 30 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 30 | 0 | 0 | 0 | 0 | 0 | 1 | 30 | 0 | 0 | 30 | 10 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 30 | 22 | 22 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 30 | 1 | 1 | 30 | 1 | 1 | 30 | 0 | 0 | 0 | 0 | 30 | 7 | 7 | 0 | 0 | 1 | 8 | 29 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 30 | 30 | 30 | 30 | 30 | 2 |
| Lesotho | 29 | 29 | 29 | 29 | 0 | 29 | 1 | 29 | 29 | 29 | 29 | 29 | 25 | 25 | 29 | 29 | 0 | 3 | 29 | 29 | 29 | 29 | 1 | 1 | 29 | 29 | 29 | 29 | 29 | 0 | 0 | 0 | 0 | 29 | 29 | 28 | 28 | 8 | 29 | 29 | 29 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 4 | 0 | 0 | 0 | 0 | 3 | 29 | 0 | 0 | 29 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 29 | 21 | 21 | 5 | 5 | 4 | 4 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 23 | 28 | 7 | 3 | 27 | 7 | 3 | 29 | 6 | 6 | 2 | 5 | 29 | 2 | 2 | 1 | 2 | 0 | 1 | 27 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 3 | 3 | 1 | 29 | 29 | 29 | 29 | 29 | 0 |
| Kazakhstan | 27 | 27 | 27 | 27 | 0 | 27 | 1 | 27 | 27 | 27 | 26 | 27 | 26 | 26 | 27 | 27 | 7 | 19 | 27 | 27 | 27 | 27 | 2 | 2 | 27 | 27 | 27 | 27 | 27 | 2 | 2 | 0 | 0 | 27 | 27 | 26 | 26 | 20 | 27 | 27 | 27 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 27 | 0 | 0 | 0 | 0 | 0 | 13 | 27 | 0 | 0 | 27 | 19 | 18 | 19 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 27 | 27 | 23 | 23 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 27 | 20 | 18 | 27 | 19 | 19 | 27 | 6 | 6 | 4 | 8 | 27 | 1 | 1 | 0 | 0 | 0 | 2 | 8 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 4 | 19 | 11 | 11 | 27 | 27 | 27 | 27 | 27 | 3 |
| Madagascar | 27 | 27 | 27 | 27 | 0 | 27 | 2 | 27 | 27 | 27 | 27 | 27 | 27 | 27 | 27 | 27 | 1 | 13 | 27 | 27 | 27 | 27 | 3 | 3 | 27 | 27 | 27 | 27 | 27 | 1 | 1 | 0 | 0 | 27 | 27 | 25 | 25 | 19 | 27 | 27 | 27 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 11 | 27 | 0 | 0 | 27 | 11 | 12 | 13 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 27 | 27 | 19 | 19 | 3 | 3 | 3 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 27 | 14 | 14 | 27 | 15 | 14 | 27 | 9 | 9 | 2 | 9 | 27 | 4 | 4 | 2 | 2 | 0 | 4 | 14 | 1 | 0 | 1 | 0 | 0 | 2 | 2 | 1 | 7 | 13 | 9 | 4 | 27 | 27 | 27 | 27 | 27 | 2 |
| Guyana | 26 | 26 | 26 | 26 | 0 | 26 | 0 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 26 | 4 | 12 | 26 | 26 | 26 | 26 | 4 | 4 | 26 | 26 | 26 | 26 | 26 | 1 | 1 | 0 | 0 | 26 | 26 | 26 | 26 | 22 | 26 | 26 | 26 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 26 | 0 | 0 | 0 | 0 | 0 | 10 | 26 | 0 | 0 | 26 | 9 | 8 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 26 | 26 | 21 | 21 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 26 | 12 | 11 | 25 | 12 | 10 | 26 | 5 | 5 | 1 | 3 | 26 | 3 | 3 | 1 | 0 | 0 | 1 | 15 | 2 | 1 | 1 | 1 | 0 | 2 | 2 | 2 | 4 | 12 | 12 | 7 | 26 | 26 | 26 | 26 | 26 | 2 |
| Hong Kong | 26 | 26 | 26 | 26 | 0 | 26 | 0 | 26 | 26 | 26 | 25 | 26 | 26 | 26 | 26 | 26 | 3 | 5 | 26 | 26 | 26 | 26 | 2 | 2 | 26 | 26 | 26 | 26 | 26 | 0 | 0 | 0 | 0 | 26 | 26 | 24 | 24 | 16 | 25 | 25 | 25 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 5 | 26 | 0 | 0 | 26 | 10 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 26 | 26 | 17 | 17 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 26 | 5 | 5 | 26 | 5 | 5 | 26 | 7 | 7 | 1 | 3 | 26 | 2 | 2 | 0 | 0 | 0 | 2 | 22 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 5 | 5 | 26 | 26 | 26 | 26 | 26 | 0 |
| Laos | 27 | 27 | 27 | 27 | 0 | 27 | 0 | 27 | 27 | 27 | 26 | 27 | 24 | 24 | 27 | 27 | 5 | 19 | 27 | 27 | 27 | 27 | 2 | 2 | 27 | 27 | 27 | 27 | 27 | 0 | 0 | 0 | 0 | 27 | 27 | 25 | 25 | 27 | 27 | 27 | 27 | 4 | 4 | 4 | 4 | 4 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 12 | 27 | 0 | 0 | 27 | 12 | 17 | 19 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 27 | 27 | 24 | 24 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 26 | 18 | 18 | 26 | 19 | 18 | 27 | 7 | 7 | 2 | 9 | 27 | 1 | 1 | 0 | 0 | 0 | 1 | 8 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 7 | 19 | 15 | 9 | 27 | 27 | 27 | 27 | 27 | 0 |
| Armenia | 24 | 24 | 24 | 24 | 0 | 24 | 1 | 24 | 24 | 24 | 23 | 24 | 24 | 24 | 24 | 24 | 6 | 15 | 24 | 24 | 24 | 24 | 3 | 3 | 24 | 24 | 24 | 24 | 24 | 2 | 2 | 1 | 1 | 24 | 24 | 23 | 23 | 20 | 24 | 24 | 24 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0 | 0 | 0 | 0 | 0 | 12 | 24 | 0 | 0 | 24 | 10 | 11 | 15 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 24 | 23 | 23 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 15 | 24 | 19 | 15 | 23 | 19 | 15 | 24 | 8 | 8 | 2 | 6 | 24 | 5 | 5 | 2 | 2 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 6 | 15 | 11 | 7 | 24 | 24 | 24 | 24 | 24 | 2 |
| Guinea | 25 | 25 | 25 | 25 | 1 | 25 | 2 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 5 | 18 | 25 | 25 | 25 | 25 | 2 | 2 | 25 | 25 | 25 | 25 | 25 | 3 | 3 | 0 | 0 | 25 | 25 | 25 | 25 | 24 | 25 | 25 | 25 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 25 | 0 | 0 | 0 | 0 | 0 | 15 | 25 | 0 | 0 | 25 | 15 | 15 | 18 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 25 | 25 | 18 | 18 | 4 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 24 | 18 | 14 | 20 | 18 | 13 | 25 | 8 | 8 | 3 | 6 | 25 | 3 | 3 | 1 | 3 | 1 | 1 | 10 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 2 | 9 | 18 | 13 | 8 | 25 | 25 | 25 | 25 | 25 | 4 |
| East Germany (GDR) | 38 | 38 | 38 | 38 | 0 | 38 | 0 | 38 | 38 | 38 | 37 | 38 | 38 | 38 | 38 | 38 | 0 | 0 | 38 | 38 | 38 | 38 | 4 | 4 | 38 | 38 | 38 | 38 | 38 | 0 | 0 | 0 | 0 | 38 | 38 | 35 | 35 | 11 | 37 | 36 | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 38 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 38 | 20 | 20 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 24 | 0 | 0 | 24 | 0 | 0 | 38 | 8 | 8 | 3 | 3 | 38 | 1 | 1 | 0 | 0 | 0 | 1 | 38 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 38 | 38 | 38 | 38 | 38 | 0 |
| Malta | 23 | 23 | 23 | 23 | 0 | 23 | 0 | 23 | 23 | 23 | 23 | 23 | 22 | 22 | 23 | 23 | 3 | 6 | 23 | 23 | 23 | 23 | 2 | 2 | 23 | 23 | 23 | 23 | 23 | 0 | 0 | 0 | 0 | 23 | 23 | 22 | 22 | 10 | 23 | 23 | 23 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | 3 | 23 | 0 | 0 | 23 | 7 | 6 | 6 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 23 | 23 | 19 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 23 | 6 | 6 | 23 | 6 | 6 | 23 | 6 | 6 | 6 | 6 | 23 | 1 | 1 | 1 | 0 | 1 | 1 | 18 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 6 | 5 | 3 | 23 | 23 | 23 | 23 | 23 | 3 |
| Maldives | 22 | 22 | 22 | 22 | 0 | 22 | 1 | 22 | 22 | 22 | 22 | 22 | 21 | 21 | 22 | 22 | 0 | 22 | 22 | 22 | 22 | 22 | 4 | 4 | 22 | 22 | 22 | 22 | 22 | 1 | 1 | 0 | 0 | 22 | 22 | 22 | 22 | 22 | 22 | 22 | 22 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 22 | 0 | 0 | 0 | 0 | 0 | 11 | 22 | 0 | 0 | 22 | 20 | 22 | 22 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 22 | 20 | 20 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 21 | 22 | 21 | 21 | 22 | 21 | 22 | 14 | 14 | 13 | 14 | 22 | 3 | 3 | 1 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 6 | 22 | 11 | 8 | 22 | 22 | 22 | 22 | 22 | 8 |
| Moldova | 21 | 21 | 21 | 21 | 0 | 21 | 0 | 21 | 21 | 21 | 17 | 21 | 20 | 20 | 21 | 21 | 2 | 5 | 21 | 21 | 21 | 21 | 0 | 0 | 21 | 21 | 21 | 21 | 21 | 0 | 0 | 0 | 0 | 21 | 21 | 21 | 21 | 16 | 21 | 21 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 | 0 | 4 | 21 | 0 | 0 | 21 | 3 | 4 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 21 | 21 | 19 | 19 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 21 | 5 | 4 | 21 | 5 | 4 | 21 | 4 | 4 | 3 | 2 | 21 | 2 | 2 | 0 | 0 | 0 | 2 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 3 | 21 | 21 | 21 | 21 | 21 | 2 |
| Uzbekistan | 21 | 21 | 21 | 21 | 0 | 21 | 1 | 21 | 21 | 21 | 20 | 21 | 21 | 21 | 21 | 21 | 3 | 18 | 21 | 21 | 21 | 21 | 3 | 3 | 21 | 21 | 21 | 21 | 21 | 2 | 2 | 0 | 0 | 21 | 21 | 21 | 21 | 20 | 21 | 21 | 21 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 3 | 0 | 3 | 0 | 18 | 21 | 3 | 3 | 21 | 13 | 17 | 18 | 5 | 5 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 21 | 21 | 19 | 19 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 21 | 18 | 17 | 21 | 18 | 17 | 21 | 14 | 14 | 1 | 4 | 21 | 1 | 1 | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 7 | 18 | 15 | 13 | 21 | 21 | 21 | 21 | 21 | 9 |
| United Arab Emirates | 22 | 22 | 22 | 22 | 0 | 22 | 1 | 22 | 22 | 22 | 22 | 22 | 20 | 20 | 22 | 22 | 2 | 5 | 22 | 22 | 22 | 22 | 1 | 1 | 22 | 22 | 22 | 22 | 22 | 0 | 0 | 0 | 0 | 22 | 22 | 20 | 20 | 14 | 22 | 22 | 22 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 1 | 0 | 0 | 0 | 0 | 2 | 22 | 0 | 0 | 22 | 10 | 5 | 5 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 22 | 22 | 20 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 20 | 6 | 6 | 20 | 7 | 7 | 22 | 3 | 3 | 0 | 2 | 22 | 2 | 2 | 0 | 0 | 1 | 3 | 17 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 4 | 3 | 22 | 22 | 22 | 22 | 22 | 3 |
| New Zealand | 20 | 20 | 20 | 20 | 1 | 20 | 1 | 20 | 20 | 20 | 17 | 20 | 20 | 20 | 20 | 20 | 6 | 12 | 20 | 20 | 20 | 20 | 2 | 2 | 20 | 20 | 20 | 20 | 20 | 0 | 0 | 0 | 0 | 20 | 20 | 19 | 19 | 17 | 20 | 20 | 20 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 20 | 0 | 0 | 0 | 0 | 0 | 10 | 20 | 0 | 0 | 20 | 10 | 12 | 12 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 20 | 20 | 17 | 17 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 20 | 12 | 12 | 20 | 12 | 12 | 20 | 3 | 3 | 2 | 3 | 20 | 1 | 1 | 1 | 1 | 0 | 1 | 10 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 12 | 9 | 8 | 20 | 20 | 20 | 20 | 20 | 3 |
| Djibouti | 22 | 22 | 22 | 22 | 0 | 22 | 0 | 22 | 22 | 22 | 22 | 22 | 21 | 21 | 22 | 22 | 2 | 4 | 22 | 22 | 22 | 22 | 6 | 6 | 22 | 22 | 22 | 22 | 22 | 0 | 0 | 0 | 0 | 22 | 22 | 22 | 22 | 18 | 22 | 20 | 20 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0 | 0 | 0 | 0 | 0 | 1 | 22 | 0 | 0 | 22 | 5 | 4 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 22 | 19 | 19 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 20 | 4 | 5 | 20 | 4 | 5 | 22 | 3 | 3 | 1 | 1 | 22 | 3 | 3 | 0 | 1 | 0 | 2 | 19 | 1 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 2 | 4 | 3 | 3 | 22 | 22 | 22 | 22 | 22 | 0 |
| Finland | 20 | 20 | 20 | 20 | 0 | 20 | 0 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 2 | 16 | 20 | 20 | 20 | 20 | 3 | 3 | 20 | 20 | 20 | 20 | 20 | 0 | 0 | 0 | 0 | 20 | 20 | 20 | 20 | 18 | 20 | 14 | 14 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 20 | 0 | 0 | 0 | 0 | 0 | 7 | 20 | 0 | 0 | 20 | 16 | 16 | 16 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 20 | 20 | 20 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 20 | 16 | 16 | 19 | 16 | 16 | 20 | 13 | 13 | 11 | 13 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 16 | 10 | 7 | 20 | 20 | 20 | 20 | 20 | 0 |
| Trinidad and Tobago | 22 | 22 | 22 | 22 | 1 | 22 | 0 | 22 | 22 | 22 | 20 | 22 | 22 | 22 | 22 | 22 | 1 | 7 | 22 | 22 | 22 | 22 | 1 | 1 | 22 | 22 | 22 | 22 | 22 | 0 | 0 | 0 | 0 | 22 | 22 | 20 | 20 | 18 | 22 | 22 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0 | 0 | 0 | 0 | 0 | 4 | 22 | 0 | 0 | 22 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 22 | 18 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 20 | 7 | 7 | 21 | 7 | 7 | 22 | 6 | 6 | 4 | 4 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 4 | 0 | 22 | 22 | 22 | 22 | 22 | 5 |
| Norway | 19 | 19 | 19 | 19 | 0 | 19 | 0 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 6 | 8 | 19 | 19 | 19 | 19 | 2 | 2 | 19 | 19 | 19 | 19 | 19 | 0 | 0 | 0 | 0 | 19 | 19 | 16 | 16 | 15 | 18 | 17 | 17 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 19 | 0 | 0 | 0 | 0 | 0 | 8 | 19 | 0 | 0 | 19 | 12 | 7 | 8 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 19 | 11 | 11 | 2 | 2 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 19 | 8 | 8 | 19 | 8 | 8 | 19 | 4 | 4 | 0 | 7 | 19 | 2 | 2 | 0 | 0 | 1 | 2 | 11 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 1 | 8 | 7 | 6 | 19 | 19 | 19 | 19 | 19 | 2 |
| Ghana | 19 | 19 | 19 | 19 | 0 | 19 | 0 | 19 | 19 | 19 | 19 | 19 | 17 | 16 | 19 | 19 | 0 | 2 | 19 | 19 | 19 | 19 | 0 | 0 | 19 | 19 | 19 | 19 | 19 | 1 | 1 | 0 | 0 | 19 | 19 | 19 | 19 | 16 | 19 | 19 | 19 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 1 | 0 | 0 | 0 | 0 | 0 | 19 | 0 | 0 | 19 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 19 | 16 | 16 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 19 | 3 | 2 | 19 | 2 | 2 | 19 | 3 | 3 | 3 | 1 | 19 | 1 | 1 | 1 | 0 | 0 | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 2 | 2 | 1 | 19 | 19 | 19 | 19 | 19 | 2 |
| Slovak Republic | 18 | 18 | 18 | 18 | 0 | 18 | 0 | 18 | 18 | 18 | 16 | 18 | 18 | 18 | 18 | 18 | 1 | 5 | 18 | 18 | 18 | 18 | 2 | 2 | 18 | 18 | 18 | 18 | 18 | 0 | 0 | 0 | 0 | 18 | 18 | 16 | 16 | 9 | 18 | 18 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 5 | 18 | 0 | 0 | 18 | 1 | 4 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 18 | 16 | 16 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 18 | 5 | 5 | 18 | 5 | 5 | 18 | 3 | 3 | 0 | 3 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 3 | 2 | 18 | 18 | 18 | 18 | 18 | 0 |
| Fiji | 17 | 17 | 17 | 17 | 1 | 17 | 0 | 17 | 17 | 17 | 15 | 17 | 17 | 17 | 17 | 17 | 4 | 8 | 17 | 17 | 17 | 17 | 1 | 1 | 17 | 17 | 17 | 17 | 17 | 1 | 1 | 0 | 0 | 17 | 17 | 15 | 15 | 16 | 17 | 17 | 17 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 2 | 0 | 0 | 0 | 0 | 8 | 17 | 0 | 0 | 17 | 4 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 17 | 9 | 9 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 17 | 8 | 8 | 17 | 8 | 8 | 17 | 7 | 7 | 2 | 1 | 17 | 3 | 3 | 3 | 3 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 7 | 8 | 6 | 3 | 17 | 17 | 17 | 17 | 17 | 5 |
| Mauritania | 18 | 18 | 18 | 18 | 0 | 18 | 1 | 18 | 18 | 18 | 18 | 18 | 15 | 15 | 18 | 18 | 4 | 13 | 18 | 18 | 18 | 18 | 2 | 2 | 18 | 18 | 18 | 18 | 18 | 3 | 3 | 0 | 0 | 18 | 18 | 16 | 16 | 11 | 17 | 17 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 13 | 18 | 0 | 0 | 18 | 13 | 13 | 13 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 18 | 18 | 14 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 17 | 13 | 13 | 15 | 12 | 11 | 18 | 9 | 9 | 1 | 8 | 18 | 9 | 9 | 2 | 4 | 0 | 4 | 10 | 3 | 0 | 0 | 0 | 0 | 7 | 7 | 6 | 4 | 12 | 11 | 8 | 18 | 18 | 18 | 18 | 18 | 0 |
| Latvia | 17 | 17 | 17 | 17 | 0 | 17 | 0 | 17 | 17 | 17 | 16 | 17 | 17 | 17 | 17 | 17 | 1 | 10 | 17 | 17 | 17 | 17 | 3 | 3 | 17 | 17 | 17 | 17 | 17 | 0 | 0 | 0 | 0 | 17 | 17 | 17 | 17 | 13 | 17 | 17 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 9 | 17 | 0 | 0 | 17 | 4 | 7 | 10 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 17 | 17 | 17 | 17 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 17 | 10 | 10 | 16 | 10 | 10 | 17 | 7 | 7 | 2 | 4 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 10 | 3 | 3 | 17 | 17 | 17 | 17 | 17 | 2 |
| Estonia | 16 | 16 | 16 | 16 | 0 | 16 | 0 | 16 | 16 | 16 | 14 | 16 | 13 | 13 | 16 | 16 | 3 | 5 | 16 | 16 | 16 | 16 | 3 | 3 | 16 | 16 | 16 | 16 | 16 | 1 | 1 | 0 | 0 | 16 | 16 | 11 | 11 | 14 | 16 | 11 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 4 | 16 | 0 | 0 | 16 | 4 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 16 | 16 | 12 | 12 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 16 | 5 | 5 | 16 | 5 | 5 | 16 | 3 | 3 | 2 | 3 | 16 | 1 | 1 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 4 | 3 | 16 | 16 | 16 | 16 | 16 | 0 |
| Luxembourg | 16 | 16 | 16 | 16 | 0 | 16 | 0 | 16 | 16 | 16 | 16 | 16 | 12 | 12 | 16 | 16 | 0 | 0 | 16 | 16 | 16 | 16 | 0 | 0 | 16 | 16 | 16 | 16 | 16 | 0 | 0 | 0 | 0 | 16 | 16 | 15 | 15 | 4 | 15 | 16 | 16 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 16 | 15 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 16 | 0 | 0 | 16 | 0 | 0 | 16 | 4 | 4 | 1 | 4 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 16 | 16 | 16 | 16 | 0 |
| Swaziland | 16 | 16 | 16 | 16 | 0 | 16 | 0 | 16 | 16 | 16 | 16 | 16 | 15 | 15 | 16 | 16 | 2 | 7 | 16 | 16 | 16 | 16 | 1 | 1 | 16 | 16 | 16 | 16 | 16 | 0 | 0 | 0 | 0 | 16 | 16 | 15 | 15 | 9 | 16 | 16 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 6 | 16 | 0 | 0 | 16 | 4 | 7 | 7 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 16 | 16 | 13 | 13 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 15 | 8 | 8 | 15 | 8 | 8 | 16 | 8 | 8 | 2 | 6 | 16 | 1 | 1 | 0 | 0 | 0 | 1 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 6 | 2 | 16 | 16 | 16 | 16 | 16 | 0 |
| Serbia | 12 | 12 | 12 | 12 | 0 | 12 | 0 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 8 | 12 | 12 | 12 | 12 | 12 | 1 | 1 | 12 | 12 | 12 | 12 | 12 | 2 | 2 | 0 | 0 | 12 | 12 | 12 | 12 | 11 | 12 | 12 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 6 | 12 | 0 | 0 | 12 | 10 | 12 | 12 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 12 | 11 | 11 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 10 | 10 | 2 | 8 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 12 | 11 | 7 | 12 | 12 | 12 | 12 | 12 | 2 |
| Vietnam | 12 | 12 | 12 | 12 | 0 | 12 | 0 | 12 | 12 | 12 | 11 | 12 | 11 | 11 | 12 | 12 | 1 | 6 | 12 | 12 | 12 | 12 | 0 | 0 | 12 | 12 | 12 | 12 | 12 | 0 | 0 | 0 | 0 | 12 | 12 | 11 | 11 | 11 | 12 | 12 | 12 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 4 | 12 | 0 | 0 | 12 | 6 | 6 | 6 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 12 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 12 | 7 | 6 | 12 | 7 | 6 | 12 | 1 | 1 | 1 | 1 | 12 | 1 | 1 | 0 | 0 | 1 | 1 | 10 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 6 | 6 | 6 | 12 | 12 | 12 | 12 | 12 | 0 |
| Belarus | 13 | 13 | 13 | 13 | 0 | 13 | 0 | 13 | 13 | 13 | 12 | 13 | 13 | 13 | 13 | 13 | 4 | 10 | 13 | 13 | 13 | 13 | 4 | 4 | 13 | 13 | 13 | 13 | 13 | 1 | 1 | 0 | 0 | 13 | 13 | 12 | 12 | 11 | 13 | 13 | 13 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 8 | 13 | 0 | 0 | 13 | 8 | 9 | 10 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 13 | 13 | 13 | 13 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 12 | 10 | 10 | 12 | 10 | 10 | 13 | 6 | 6 | 2 | 6 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 10 | 7 | 5 | 13 | 13 | 13 | 13 | 13 | 0 |
| Serbia-Montenegro | 11 | 11 | 11 | 11 | 0 | 11 | 0 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 2 | 11 | 11 | 11 | 11 | 11 | 2 | 2 | 11 | 11 | 11 | 11 | 11 | 1 | 1 | 0 | 0 | 11 | 11 | 10 | 10 | 11 | 11 | 11 | 11 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 11 | 11 | 0 | 0 | 11 | 5 | 10 | 11 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 11 | 11 | 11 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 11 | 10 | 11 | 11 | 10 | 11 | 11 | 7 | 7 | 3 | 6 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 11 | 9 | 7 | 11 | 11 | 11 | 11 | 11 | 0 |
| Czechoslovakia | 10 | 10 | 10 | 10 | 0 | 10 | 0 | 10 | 10 | 10 | 10 | 10 | 9 | 9 | 10 | 10 | 0 | 0 | 10 | 10 | 10 | 10 | 0 | 0 | 10 | 10 | 10 | 10 | 10 | 0 | 0 | 0 | 0 | 10 | 10 | 10 | 10 | 7 | 10 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 10 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 10 | 0 | 0 | 10 | 0 | 0 | 10 | 1 | 1 | 1 | 0 | 10 | 2 | 2 | 0 | 0 | 1 | 2 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 10 | 10 | 10 | 0 |
| East Timor | 10 | 10 | 10 | 10 | 0 | 10 | 0 | 10 | 10 | 10 | 10 | 10 | 8 | 8 | 10 | 10 | 3 | 10 | 10 | 10 | 10 | 10 | 1 | 1 | 10 | 10 | 10 | 10 | 10 | 1 | 1 | 0 | 0 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 0 | 0 | 10 | 4 | 10 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 10 | 10 | 10 | 10 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 10 | 10 | 9 | 9 | 10 | 8 | 10 | 2 | 2 | 0 | 2 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 10 | 7 | 5 | 10 | 10 | 10 | 10 | 10 | 0 |
| Guinea-Bissau | 9 | 9 | 9 | 9 | 0 | 9 | 1 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 0 | 9 | 9 | 9 | 9 | 9 | 1 | 1 | 9 | 9 | 9 | 9 | 9 | 1 | 1 | 0 | 0 | 9 | 9 | 9 | 9 | 8 | 9 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 9 | 9 | 0 | 0 | 9 | 3 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 9 | 9 | 9 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 9 | 9 | 5 | 8 | 9 | 4 | 9 | 7 | 7 | 4 | 3 | 9 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 5 | 9 | 7 | 2 | 9 | 9 | 9 | 9 | 9 | 4 |
| Eritrea | 10 | 10 | 10 | 10 | 1 | 10 | 1 | 10 | 10 | 10 | 10 | 10 | 6 | 6 | 10 | 10 | 5 | 7 | 10 | 10 | 10 | 10 | 1 | 1 | 10 | 10 | 10 | 10 | 10 | 1 | 1 | 0 | 0 | 10 | 10 | 10 | 10 | 5 | 10 | 10 | 10 | 2 | 2 | 2 | 2 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 1 | 0 | 0 | 0 | 7 | 10 | 1 | 0 | 10 | 5 | 7 | 7 | 3 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 5 | 10 | 10 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 9 | 7 | 5 | 9 | 7 | 5 | 10 | 4 | 4 | 0 | 5 | 10 | 2 | 2 | 1 | 1 | 0 | 1 | 4 | 1 | 0 | 1 | 0 | 0 | 2 | 2 | 1 | 3 | 7 | 4 | 1 | 10 | 10 | 10 | 10 | 10 | 2 |
| Botswana | 10 | 10 | 10 | 10 | 0 | 10 | 0 | 10 | 10 | 10 | 10 | 10 | 9 | 9 | 10 | 10 | 0 | 0 | 10 | 10 | 10 | 10 | 0 | 0 | 10 | 10 | 10 | 10 | 10 | 0 | 0 | 0 | 0 | 10 | 10 | 9 | 9 | 4 | 9 | 10 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 10 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 8 | 0 | 2 | 8 | 0 | 2 | 10 | 3 | 3 | 2 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 10 | 10 | 10 | 0 |
| Gabon | 8 | 8 | 8 | 8 | 0 | 8 | 0 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 2 | 4 | 8 | 8 | 8 | 8 | 0 | 0 | 8 | 8 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 8 | 8 | 7 | 7 | 7 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 3 | 8 | 0 | 0 | 8 | 4 | 4 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 8 | 8 | 8 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 7 | 8 | 4 | 4 | 8 | 4 | 4 | 8 | 1 | 1 | 1 | 1 | 8 | 2 | 2 | 2 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 4 | 3 | 1 | 8 | 8 | 8 | 8 | 8 | 2 |
| Belize | 8 | 8 | 8 | 8 | 0 | 8 | 3 | 8 | 8 | 8 | 6 | 8 | 7 | 7 | 8 | 8 | 1 | 2 | 8 | 8 | 8 | 8 | 1 | 1 | 8 | 8 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 8 | 8 | 6 | 6 | 6 | 8 | 8 | 8 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 1 | 8 | 0 | 0 | 8 | 4 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 8 | 8 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 8 | 2 | 3 | 8 | 2 | 2 | 8 | 3 | 3 | 1 | 2 | 8 | 2 | 2 | 2 | 2 | 0 | 3 | 6 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 1 | 2 | 2 | 2 | 8 | 8 | 8 | 8 | 8 | 0 |
| Lithuania | 8 | 8 | 8 | 8 | 0 | 8 | 0 | 8 | 8 | 8 | 7 | 8 | 8 | 8 | 8 | 8 | 0 | 0 | 8 | 8 | 8 | 8 | 1 | 1 | 8 | 8 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 8 | 8 | 8 | 8 | 6 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 8 | 0 | 0 | 8 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 8 | 8 | 8 | 8 | 0 |
| Benin | 8 | 8 | 8 | 8 | 0 | 8 | 0 | 8 | 8 | 8 | 8 | 8 | 7 | 7 | 8 | 8 | 1 | 1 | 8 | 8 | 8 | 8 | 0 | 0 | 8 | 8 | 8 | 8 | 8 | 0 | 0 | 0 | 0 | 8 | 8 | 8 | 8 | 4 | 8 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 1 | 8 | 0 | 0 | 8 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 8 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 8 | 1 | 1 | 7 | 1 | 1 | 8 | 0 | 0 | 0 | 0 | 8 | 1 | 1 | 0 | 0 | 0 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 | 8 | 8 | 8 | 8 | 0 |
| Martinique | 12 | 12 | 12 | 12 | 0 | 12 | 0 | 12 | 12 | 12 | 10 | 12 | 11 | 11 | 12 | 12 | 0 | 0 | 12 | 12 | 12 | 12 | 0 | 0 | 12 | 12 | 12 | 12 | 12 | 0 | 0 | 0 | 0 | 12 | 12 | 11 | 11 | 4 | 12 | 12 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 1 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 12 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 12 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 8 | 0 | 0 | 8 | 0 | 0 | 12 | 6 | 6 | 2 | 1 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 12 | 12 | 12 | 12 | 0 |
| Qatar | 7 | 7 | 7 | 7 | 0 | 7 | 0 | 7 | 7 | 7 | 7 | 7 | 6 | 6 | 7 | 7 | 1 | 4 | 7 | 7 | 7 | 7 | 2 | 2 | 7 | 7 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 7 | 7 | 7 | 7 | 4 | 7 | 7 | 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 3 | 7 | 0 | 0 | 7 | 5 | 4 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 7 | 7 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 7 | 4 | 4 | 7 | 4 | 4 | 7 | 1 | 1 | 1 | 2 | 7 | 1 | 1 | 1 | 0 | 1 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 4 | 3 | 2 | 7 | 7 | 7 | 7 | 7 | 0 |
| Singapore | 7 | 7 | 7 | 7 | 0 | 7 | 0 | 7 | 7 | 7 | 6 | 7 | 7 | 7 | 7 | 7 | 0 | 0 | 7 | 7 | 7 | 7 | 1 | 1 | 7 | 7 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 7 | 7 | 7 | 7 | 4 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 7 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 7 | 5 | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 7 | 0 | 1 | 7 | 0 | 0 | 7 | 1 | 1 | 0 | 0 | 7 | 2 | 2 | 0 | 0 | 1 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 7 | 7 | 7 | 7 | 7 | 0 |
| Romania | 6 | 6 | 6 | 6 | 0 | 6 | 0 | 6 | 6 | 6 | 5 | 6 | 6 | 6 | 6 | 6 | 1 | 1 | 6 | 6 | 6 | 6 | 0 | 0 | 6 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 6 | 6 | 5 | 5 | 2 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 6 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 1 | 1 | 6 | 1 | 1 | 6 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 6 | 6 | 6 | 6 | 6 | 0 |
| Brunei | 6 | 6 | 6 | 6 | 0 | 6 | 0 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 5 | 5 | 6 | 6 | 6 | 6 | 0 | 0 | 6 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 6 | 6 | 5 | 5 | 5 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 6 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 5 | 5 | 6 | 5 | 5 | 6 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 4 | 6 | 6 | 6 | 6 | 6 | 5 |
| North Yemen | 6 | 6 | 6 | 6 | 0 | 6 | 0 | 6 | 6 | 6 | 6 | 6 | 4 | 4 | 6 | 6 | 0 | 0 | 6 | 6 | 6 | 6 | 0 | 0 | 6 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 6 | 6 | 6 | 6 | 4 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 6 | 1 | 2 | 6 | 1 | 2 | 6 | 1 | 1 | 1 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 6 | 6 | 6 | 0 |
| Slovenia | 6 | 6 | 6 | 6 | 0 | 6 | 0 | 6 | 6 | 6 | 5 | 6 | 6 | 6 | 6 | 6 | 0 | 1 | 6 | 6 | 6 | 6 | 1 | 1 | 6 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 6 | 6 | 6 | 6 | 4 | 5 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 6 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 6 | 1 | 1 | 6 | 1 | 1 | 6 | 1 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 6 | 6 | 6 | 6 | 0 |
| Comoros | 5 | 5 | 5 | 5 | 0 | 5 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 5 | 5 | 5 | 5 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 0 | 0 | 5 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 2 |
| Malawi | 5 | 5 | 5 | 5 | 0 | 5 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 1 | 2 | 5 | 5 | 5 | 5 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 5 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 2 | 2 | 5 | 2 | 2 | 5 | 2 | 2 | 1 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 5 | 5 | 5 | 5 | 5 | 0 |
| French Guiana | 7 | 7 | 7 | 7 | 0 | 7 | 0 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 0 | 0 | 7 | 7 | 7 | 7 | 0 | 0 | 7 | 7 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 7 | 7 | 7 | 7 | 4 | 7 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 7 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 0 | 0 | 5 | 0 | 0 | 7 | 3 | 3 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 7 | 7 | 7 | 7 | 0 |
| Montenegro | 5 | 5 | 5 | 5 | 0 | 5 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 2 | 5 | 5 | 5 | 5 | 5 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 4 | 4 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 0 | 0 | 5 | 4 | 4 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 2 | 3 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 4 | 2 | 5 | 5 | 5 | 5 | 5 | 0 |
| Bhutan | 6 | 6 | 6 | 6 | 0 | 6 | 0 | 6 | 6 | 6 | 6 | 6 | 5 | 5 | 6 | 6 | 2 | 6 | 6 | 6 | 6 | 6 | 0 | 0 | 6 | 6 | 6 | 6 | 6 | 1 | 1 | 0 | 0 | 6 | 6 | 6 | 6 | 5 | 6 | 6 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 6 | 6 | 0 | 0 | 6 | 3 | 5 | 6 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 6 | 6 | 6 | 6 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 6 | 5 | 5 | 6 | 5 | 6 | 3 | 3 | 0 | 2 | 6 | 1 | 1 | 0 | 1 | 0 | 0 | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 6 | 4 | 3 | 6 | 6 | 6 | 6 | 6 | 0 |
| Grenada | 5 | 5 | 5 | 5 | 0 | 5 | 0 | 5 | 5 | 5 | 4 | 5 | 4 | 4 | 5 | 5 | 0 | 0 | 5 | 5 | 5 | 5 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 4 | 4 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 0 | 1 | 5 | 0 | 0 | 5 | 2 | 2 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 0 |
| Bahamas | 5 | 5 | 5 | 5 | 0 | 5 | 0 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 | 0 | 1 | 5 | 5 | 5 | 5 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 5 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 1 | 1 | 5 | 1 | 1 | 5 | 2 | 2 | 2 | 2 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 5 | 5 | 5 | 5 | 5 | 0 |
| Iceland | 4 | 4 | 4 | 4 | 0 | 4 | 0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 0 | 2 | 4 | 4 | 4 | 4 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 4 | 4 | 3 | 3 | 2 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 4 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 2 | 2 | 4 | 2 | 2 | 4 | 1 | 1 | 1 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 4 | 4 | 4 | 4 | 4 | 0 |
| Western Sahara | 5 | 5 | 5 | 5 | 0 | 5 | 1 | 5 | 5 | 5 | 5 | 5 | 3 | 3 | 5 | 5 | 1 | 1 | 5 | 5 | 5 | 5 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 3 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 5 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 1 | 1 | 4 | 1 | 1 | 5 | 0 | 0 | 0 | 1 | 5 | 1 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 5 | 5 | 5 | 5 | 5 | 0 |
| Solomon Islands | 4 | 4 | 4 | 4 | 0 | 4 | 0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 1 | 4 | 4 | 4 | 4 | 4 | 1 | 1 | 4 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 4 | 2 | 3 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 4 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 3 | 4 | 2 | 2 | 0 | 0 | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 2 | 4 | 2 | 2 | 4 | 4 | 4 | 4 | 4 | 0 |
| French Polynesia | 3 | 3 | 3 | 3 | 0 | 3 | 0 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 0 |
| People's Republic of the Congo | 4 | 4 | 4 | 4 | 0 | 4 | 1 | 4 | 4 | 4 | 4 | 4 | 3 | 3 | 4 | 4 | 0 | 1 | 4 | 4 | 4 | 4 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 4 | 3 | 4 | 4 | 4 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 4 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 1 | 3 | 1 | 1 | 4 | 0 | 0 | 0 | 0 | 4 | 2 | 2 | 0 | 0 | 0 | 2 | 3 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 4 | 4 | 4 | 4 | 4 | 1 |
| Gambia | 3 | 3 | 3 | 3 | 0 | 3 | 0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 0 | 1 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 3 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 3 | 3 | 3 | 3 | 3 | 0 |
| Barbados | 3 | 3 | 3 | 3 | 0 | 3 | 0 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 0 | 3 | 3 | 3 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 3 | 0 | 0 | 3 | 2 | 2 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 0 |
| Vanuatu | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 |
| Seychelles | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 2 | 1 | 1 | 1 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 |
| Dominica | 3 | 3 | 3 | 3 | 0 | 3 | 0 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 1 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 2 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 0 |
| Turkmenistan | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 0 |
| South Yemen | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 |
| St. Kitts and Nevis | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 |
| Equatorial Guinea | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 0 | 0 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 2 | 2 | 2 | 2 | 0 |
| Mauritius | 2 | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 0 |
| Vatican City | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| International | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| South Vietnam | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| Wallis and Futuna | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| North Korea | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| New Hebrides | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| St. Lucia | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| Antigua and Barbuda | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| Falkland Islands | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
| Andorra | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 |
import pandas as pd
import numpy as np
# Parameters
n_rows = 10**7 # Number of rows
n_folders = 200 # Number of folders
# Generate synthetic data
np.random.seed(0) # Set a seed for reproducibility
data = {
'folders': np.random.choice(['folders_' + str(i) for i in range(n_folders)], size=n_rows),
'files': np.random.randint(1, 1000, size=n_rows) # Generating random integers between 1 and 1000
}
# Create a DataFrame
dump_df = pd.DataFrame(data)
# Save to CSV for use with Dask
dump_df.to_csv('lrg_dataset.csv', index=False)
dump_df
| folders | files | |
|---|---|---|
| 0 | folders_172 | 909 |
| 1 | folders_47 | 312 |
| 2 | folders_117 | 115 |
| 3 | folders_192 | 424 |
| 4 | folders_67 | 678 |
| ... | ... | ... |
| 9999995 | folders_41 | 342 |
| 9999996 | folders_51 | 591 |
| 9999997 | folders_14 | 239 |
| 9999998 | folders_39 | 551 |
| 9999999 | folders_139 | 486 |
10000000 rows × 2 columns
import dask.dataframe as dd
import os
import time
# Parameters
n_rows = 10**7 # Number of rows
n_folders = 200 # Number of folders
# Path to the dataset
file_path = 'lrg_dataset.csv'
# Timing and memory usage comparison
start_time = time.time()
# Read the CSV file into a Pandas DataFrame
df_pandas = pd.read_csv(file_path)
pandas_duration = time.time() - start_time
# Perform groupby operation with Pandas
start_time = time.time()
df_grouped_pandas = df_pandas.groupby('folders').agg({'files': 'sum'}).reset_index()
pandas_groupby_duration = time.time() - start_time
# Estimate memory usage for Pandas DataFrame
pandas_memory_usage = df_pandas.memory_usage(deep=True).sum() / 1e6
pandas_grouped_memory_usage = df_grouped_pandas.memory_usage(deep=True).sum() / 1e6
# -----------------------------------------------------------------------------------------------------------------------------------------------------------------
# Dask operations
start_time = time.time()
# Read the CSV file into a Dask DataFrame
df_dask = dd.read_csv(file_path)
dask_duration = time.time() - start_time
# Perform groupby operation with Dask
start_time = time.time()
df_grouped_dask = df_dask.groupby('folders').agg({'files': 'sum'}).compute()
dask_groupby_duration = time.time() - start_time
# Estimate memory usage for Dask DataFrame (approximation)
sample = df_dask.head(10000000)
sample_memory_usage = sample.memory_usage(deep=True).sum()
dask_memory_usage = sample_memory_usage * (len(df_dask) / len(sample)) / 1e6
# Print results
print(f"Pandas Load Duration: {pandas_duration:.2f} seconds")
print(f"Pandas Groupby Duration: {pandas_groupby_duration:.2f} seconds")
print(f"Pandas Memory Usage: {pandas_memory_usage:.2f} MB")
print(f"Pandas Grouped Data Memory Usage: {pandas_grouped_memory_usage:.2f} MB")
print("--"*50)
print(f"Dask Load Duration: {dask_duration:.2f} seconds")
print(f"Dask Groupby Duration: {dask_groupby_duration:.2f} seconds")
print(f"Dask Memory Usage (approx): {dask_memory_usage:.2f} MB")
Pandas Load Duration: 4.02 seconds Pandas Groupby Duration: 1.05 seconds Pandas Memory Usage: 754.50 MB Pandas Grouped Data Memory Usage: 0.02 MB ---------------------------------------------------------------------------------------------------- Dask Load Duration: 0.03 seconds Dask Groupby Duration: 5.12 seconds Dask Memory Usage (approx): 754.50 MB